AI写作：技能检索进度反馈与超时兜底
检索等待时 SkillStepsCard 显示 spinner；单技能检索 25s 超时避免卡死；前后端增加 skill-timing 调试日志。 Co-authored-by: Cursor <cursoragent@cursor.com>
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@@ -32,6 +32,14 @@ export function SkillStepsCard({ steps, inProgress }: SkillStepsCardProps) {
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const [expanded, setExpanded] = useState(true);
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if (steps.length === 0 && !inProgress) return null;
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// 进行中的步骤：最后一条是 invoke_skill 且还没出对应的 skill_result，
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// 说明该技能的 web 检索正在 await 中——把它的序号徽标换成 spinner，
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// 让每次（可能数秒~数十秒的）检索等待都有"正在调用技能X检索中"的实时反馈。
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const activeIdx =
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inProgress && steps.length > 0 && steps[steps.length - 1].type === 'invoke_skill'
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? steps.length - 1
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: -1;
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return (
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<div className="rounded-md border border-violet-200 bg-violet-50/40 dark:border-violet-900/40 dark:bg-violet-950/20 overflow-hidden">
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<button
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@@ -63,19 +71,22 @@ export function SkillStepsCard({ steps, inProgress }: SkillStepsCardProps) {
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const label = STEP_LABEL[step.type] ?? step.type;
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const isStopEarly = step.type === 'stop_early';
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const isResult = step.type === 'skill_result';
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const isActive = idx === activeIdx;
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return (
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<li key={idx} className="flex items-start gap-2 text-[11px]">
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<span
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className={cn(
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'shrink-0 mt-0.5 inline-flex items-center justify-center size-4 rounded-full text-[10px] font-medium',
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isStopEarly
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? 'bg-amber-100 dark:bg-amber-900/60 text-amber-600 dark:text-amber-300'
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: isResult
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? 'bg-emerald-100 dark:bg-emerald-900/60 text-emerald-600 dark:text-emerald-300'
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: 'bg-violet-100 dark:bg-violet-900/60 text-violet-600 dark:text-violet-300',
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isActive
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? 'bg-violet-500 text-white'
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: isStopEarly
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? 'bg-amber-100 dark:bg-amber-900/60 text-amber-600 dark:text-amber-300'
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: isResult
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? 'bg-emerald-100 dark:bg-emerald-900/60 text-emerald-600 dark:text-emerald-300'
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: 'bg-violet-100 dark:bg-violet-900/60 text-violet-600 dark:text-violet-300',
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)}
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>
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{idx + 1}
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{isActive ? <Loader2 className="size-2.5 animate-spin" /> : idx + 1}
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</span>
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<div className="flex-1 min-w-0">
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<div
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@@ -137,7 +148,9 @@ export function SkillStepsCard({ steps, inProgress }: SkillStepsCardProps) {
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))}
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</>
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)}
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{inProgress && steps.length > 0 && (
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{inProgress && steps.length > 0 && activeIdx < 0 && (
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// 当前不在某个技能的检索等待中（最后一条是 result / select 等），
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// 提示还有后续步骤；正在检索时由该步自身的 spinner 体现进行中，不重复。
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<li className="flex items-center gap-2 text-[11px] text-muted-foreground italic">
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<Loader2 className="size-3 animate-spin shrink-0" />
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更多步骤生成中…
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@@ -634,12 +634,25 @@ export function useAIWriting() {
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dispatch({ type: 'set_session', sessionId: meta.thread_id });
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},
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onCustomEvent(event: unknown) {
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// 技能检索时机 trace：开 window.__AI_WRITING_DEBUG__ = true 后，控制台按
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// performance.now() 打印每条 custom 事件到达时刻 + type，用来钉死
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// skill_search_step（技能检索过程卡）与 skill_select_chunk（筛选技能打字）
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// 的实际到达顺序，区分"后端未实时 emit" vs "前端渲染滞后"。
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if (typeof window !== 'undefined' && (window as { __AI_WRITING_DEBUG__?: boolean }).__AI_WRITING_DEBUG__) {
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const t = (event as { type?: string } | null)?.type;
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// eslint-disable-next-line no-console
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console.debug(`[ai-writing:custom] +${Math.round(performance.now())}ms type=${t ?? '?'}`, event);
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}
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const pe = customToProgressEvent(event, state.sessionId ?? '');
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if (pe) dispatch({ type: 'handle', event: pe });
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},
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onUpdateEvent(data: Record<string, unknown> | undefined) {
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// data 形如 { node_name: { ...node_output } }，可能多个节点同一 step
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if (!data) return;
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if (typeof window !== 'undefined' && (window as { __AI_WRITING_DEBUG__?: boolean }).__AI_WRITING_DEBUG__) {
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// eslint-disable-next-line no-console
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console.debug(`[ai-writing:updates] +${Math.round(performance.now())}ms nodes=${Object.keys(data).join(',')}`);
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}
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// 兜底：updates 通道可能直接带 ``__interrupt__`` 数组（节点进 interrupt 时
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// LangGraph 推 ``{__interrupt__: [...]}``），SDK 主路径只在 values 通道
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// 暴露 stream.interrupt —— 万一后端 serialize 又把 __interrupt__ 滤掉，
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+24
-4
@@ -4,6 +4,7 @@ from __future__ import annotations
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import asyncio
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import logging
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import re
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import time
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import uuid
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from typing import List
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@@ -29,6 +30,11 @@ _MAX_MAX_MATERIALS = 100
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_DEFAULT_KEYWORD_COUNT = 6
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_MAX_KEYWORD_COUNT = 10
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# 单个技能的 web 检索超时（秒）。技能检索是顺序循环、最多 5 个技能，
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# DuckDuckGo 等 provider 偶发限速/挂起会把整段"技能检索过程"拖到几十秒看起来卡死；
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# 超时即放弃该技能、emit 0 条结果继续下一个，保证时间线持续推进。
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_SKILL_SEARCH_TIMEOUT_SECONDS = 25
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def _resolve_keyword_count(raw: object) -> int:
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"""把用户传入的检索关键词数量规整到 [1, _MAX_KEYWORD_COUNT]，非法值回退默认 6。"""
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@@ -178,6 +184,10 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
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def _emit_skill_step(step: dict) -> None:
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skill_steps.append(step)
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# 技能检索时机 trace：grep "[skill-timing]" 看每步 emit 的真实时刻，
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# 与前端 [ai-writing:custom] 日志对齐，钉死是否实时推送。
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logger.info("[skill-timing] t=%.3f emit skill_search_step type=%s skill=%s",
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time.monotonic(), step.get("type"), step.get("skill_name") or "")
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writer({"type": "skill_search_step", "agent_name": "素材收集专家", **step})
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if is_skill_search:
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@@ -205,15 +215,25 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
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for entry in selected:
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sk = entry["skill"]
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# 用 LLM 把 user_query + skill.description 转成针对性关键词
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skill_query = f"{user_query} {sk.description or ''}".strip() or intent
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# invoke_skill 步骤带上实际检索词（截断防过长），让用户在每次长 await 之前
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# 就看到"这一步在用技能 X 检索什么"，配合前端 spinner 体现进行中。
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_emit_skill_step({
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"type": "invoke_skill",
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"message": f"使用技能「{sk.name}」开始检索…",
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"message": f"使用技能「{sk.name}」检索：{skill_query[:60]}",
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"skill_name": sk.name,
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})
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skill_query = f"{user_query} {sk.description or ''}".strip() or intent
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try:
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res = await web_provider.search(skill_query, max_results=per_skill)
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# 加超时兜底：单个技能检索最多等 _SKILL_SEARCH_TIMEOUT_SECONDS 秒，
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# 超时不再阻塞后续技能，避免整段过程看起来"冻结"。
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res = await asyncio.wait_for(
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web_provider.search(skill_query, max_results=per_skill),
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timeout=_SKILL_SEARCH_TIMEOUT_SECONDS,
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)
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except asyncio.TimeoutError:
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logger.warning("skill[%s] search timed out after %ss", sk.name, _SKILL_SEARCH_TIMEOUT_SECONDS)
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search_errors.append(f"skill[{sk.name}]: 检索超时（>{_SKILL_SEARCH_TIMEOUT_SECONDS}s）")
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res = []
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except Exception as e:
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logger.warning("skill[%s] search failed: %s", sk.name, e)
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search_errors.append(f"skill[{sk.name}]: {e}")
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+8
@@ -16,6 +16,7 @@
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from __future__ import annotations
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import logging
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import time
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from typing import Callable, List, Optional
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from langchain_core.messages import HumanMessage, SystemMessage
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@@ -140,7 +141,14 @@ class SkillPicker:
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parser = ThinkTagParser()
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full_text = ""
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_chunk_count = {"n": 0}
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def _emit_chunk(text: str, is_thinking: bool) -> None:
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# 只在首个 chunk 打一条 timing，避免逐 token 刷屏；用来和 skill_search_step
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# 的 [skill-timing] 对比"选择打字"与"步骤事件"谁先到。
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if _chunk_count["n"] == 0:
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logger.info("[skill-timing] t=%.3f first skill_select_chunk", time.monotonic())
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_chunk_count["n"] += 1
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writer({
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"type": "skill_select_chunk",
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"text": text,
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