数值全部在服务端算好再交给模型,模型只做解读。让模型从 CSV 里自己推 z 分数,它算错的次数足以让简报引用图表反驳它的数字。 - services/insights.py:z 分数(28 天个人基线,且**排除当天**——用一个 值参与算出来的均值去衡量它自己,会把真实离群点摊平)、13 个月趋势斜率 (按序数日期最小二乘,手表放充电器上一周不会压缩 x 轴)、近 7 天活动量 对比。 - services/coach.py:三套提示词 + 回复解析,每套都配一个规则引擎版本。 网关一次生成要几分钟,上游被限流时给一个朴素的答案,好过给一张空卡片。 - services/ai.py:多轮 chat()、SSE stream()、complete()/stream_chat(), 以及 extract_json()——上游是推理模型,可见输出以思维链开头,所以从末尾 倒着找最后一个配平的 JSON(字符串感知,扛得住引号里的 } 和转义引号)。 - 接口 briefing / trend-insight / copilot(SSE),缓存表 ai_insights。 - 前端:今日页晨报卡(后台生成 + 轮询升级)、全局 Copilot 浮窗、指标详情 页归因面板。features.ai 打开。 实测(对着自建 ai-gateway):晨报一次 273 秒,缓存命中 18 毫秒——所以简报 绝不能同步阻塞首屏。网关的流式通道比阻塞通道更不可靠:同一条提示词流式 139 秒后返回「所有模型均不可用」,阻塞则成功,因此 stream_chat() 在流式零 输出时对同一模型退回非流式重试。Copilot 实测 TTFB 9ms、全程 40 秒。 顺带修两处:refresh 原来只跳过缓存读、不删行,导致「重新生成」后的轮询读 到旧行、看到 cached 就停了,用户一直盯着他刚要求替换掉的那段字;基线零方差 时原来返回 z=0.0,把「和每一条观测都不同」标成「完全正常」,改为 z=null。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
149 lines
4.7 KiB
TypeScript
149 lines
4.7 KiB
TypeScript
import { useEffect, useRef, useState } from 'react';
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import {
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apiClient, errorMessage, InsightMeta, TrendInsight as Insight,
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} from '../services/api';
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import './TrendInsight.css';
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/**
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* Metric ids the backend's feature engineering knows, keyed by the id this app
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* uses. Two names differ (the sleep shares), the rest are identical.
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*
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* Kept as an explicit list rather than sent optimistically: the endpoint 400s
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* on an unknown metric, and a button that reliably fails is worse than no
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* button on the metrics this cannot explain.
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*/
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const BACKEND_METRIC: Record<string, string> = {
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steps: 'steps',
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intensityMinutes: 'intensityMinutes',
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heartRate: 'heartRate',
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heartRateVariability: 'heartRateVariability',
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stress: 'stress',
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bodyBatteryHigh: 'bodyBatteryHigh',
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respirationAvg: 'respirationAvg',
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sleepDuration: 'sleepDuration',
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sleepQuality: 'sleepQuality',
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deepShare: 'sleepDeepPct',
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remShare: 'sleepRemPct',
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trainingReadiness: 'trainingReadiness',
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enduranceScore: 'enduranceScore',
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};
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export function supportsInsight(metricId: string) {
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return metricId in BACKEND_METRIC;
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}
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const CONFIDENCE_LABEL: Record<string, string> = {
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high: '证据充分', medium: '证据一般', low: '证据薄弱',
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};
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interface Props {
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/** This app's metric id, e.g. `heartRateVariability`. */
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metricId: string;
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startDate: string;
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endDate: string;
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}
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/**
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* AI attribution for the span currently on the chart.
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*
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* The product spec asks for this on a brush selection over a desktop chart.
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* On a phone the equivalent gesture is the range selector that is already
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* there, so the panel explains whatever window the user has selected rather
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* than adding a drag interaction that fights the page's own scrolling.
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*
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* On demand, never on load: one generation takes minutes on the gateway, so
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* running it for every metric a user browses past would spend that on nothing.
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*/
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function TrendInsight({ metricId, startDate, endDate }: Props) {
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const [insight, setInsight] = useState<Insight | null>(null);
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const [meta, setMeta] = useState<InsightMeta | null>(null);
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const [loading, setLoading] = useState(false);
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const [error, setError] = useState('');
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const live = useRef(true);
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useEffect(() => {
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live.current = true;
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return () => { live.current = false; };
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}, []);
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// A new window is a different question; clear the old answer rather than
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// leaving it under a chart it no longer describes.
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useEffect(() => {
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setInsight(null);
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setMeta(null);
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setError('');
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}, [metricId, startDate, endDate]);
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const run = async (refresh?: boolean) => {
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const backend = BACKEND_METRIC[metricId];
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if (!backend || loading) return;
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setLoading(true);
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setError('');
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try {
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const data = await apiClient.getTrendInsight(backend, startDate, endDate, refresh);
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if (!live.current) return;
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setInsight(data.insight);
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setMeta(data.meta);
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} catch (err: any) {
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if (live.current) setError(errorMessage(err, '归因分析失败'));
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} finally {
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if (live.current) setLoading(false);
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}
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};
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if (!supportsInsight(metricId)) return null;
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return (
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<section className="ti">
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<div className="ti-head">
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<h3 className="sec-title">AI 归因</h3>
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{insight && !loading && (
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<button className="ti-link" onClick={() => run(true)}>重新分析</button>
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)}
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</div>
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{!insight && !loading && !error && (
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<>
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<p className="ti-hint">
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分析 {startDate} ~ {endDate} 这段区间内该指标的变化及其关联因素。
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</p>
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<button className="ti-run" onClick={() => run()}>分析这段区间</button>
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</>
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)}
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{loading && (
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<div className="ti-loading">
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<span className="ti-spinner" aria-hidden="true" />
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正在分析,通常需要 2–5 分钟
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</div>
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)}
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{error && <p className="ti-error">{error}</p>}
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{insight && (
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<div className="ti-body">
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{insight.summary && <p className="ti-summary">{insight.summary}</p>}
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{insight.drivers.map((d) => (
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<div className="ti-driver" key={d.factor + d.detail}>
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<span className="ti-factor">{d.factor}</span>
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<p>{d.detail}</p>
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</div>
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))}
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{insight.caution && <p className="ti-caution">{insight.caution}</p>}
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<div className="ti-foot">
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<span>
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{meta?.source === 'ai' ? `AI 生成${meta.upstream ? ` · ${meta.upstream}` : ''}` : '规则引擎'}
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</span>
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<span>{CONFIDENCE_LABEL[insight.confidence]}</span>
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</div>
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</div>
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)}
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</section>
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);
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}
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export default TrendInsight;
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