feat(ai): AI 教练 —— 晨间简报、运动处方、趋势归因与 Copilot
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>
This commit is contained in:
ericwyuan
@@ -332,6 +332,106 @@ export interface TrendPoint {
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value: number;
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}
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// --- AI coach -------------------------------------------------------------
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/** How far one of today's metrics sits from the user's own recent baseline. */
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export interface Deviation {
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metric: string;
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label: string;
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unit: string;
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value: number;
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baselineMean: number | null;
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sd: number | null;
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baselineDays?: number;
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z: number | null;
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verdict: string;
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}
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export interface TrendSummary {
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metric: string;
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label: string;
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unit: string;
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days: number;
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samples: number;
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firstMean: number;
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lastMean: number;
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delta: number;
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slopePer30d: number | null;
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direction?: string;
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}
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/** The computed features a briefing was derived from — the same numbers the
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* card quotes, so the UI can show them without a second request. */
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export interface BriefingContext {
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snapshotDate: string;
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userProfile: Record<string, number | string | null>;
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todayMetrics: {
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sleep: Record<string, number | number[] | null> | null;
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autonomicNervous: Record<string, number | null>;
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recovery: Record<string, number | null>;
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activityToday: Record<string, number | null>;
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};
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deviations: Deviation[];
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trends: TrendSummary[];
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activityShift: Record<string, {
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label: string; recentMean: number; priorMean: number; changePct: number | null;
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}>;
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recentActivities: Array<Record<string, string | number | null>>;
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dataQuality: { totalDays: number; firstDate: string; lastDate: string; staleDays: number };
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}
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export interface Briefing {
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status: string;
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headline: string | null;
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diagnosis: Array<{ title: string; detail: string }>;
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shortfall: string | null;
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prescription: {
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intensity: string | null;
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hrZone: string | null;
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suggestion: string | null;
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durationMin: number | null;
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avoid: string | null;
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};
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actions: string[];
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}
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/** `source` says who answered: the model, or the rule engine standing in for
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* it. `pending` means the card on screen is the placeholder and the model's
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* version is still being generated — poll again. */
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export interface InsightMeta {
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source: 'ai' | 'rules' | 'none';
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model?: string | null;
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upstream?: string | null;
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cached?: boolean;
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pending?: boolean;
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generating?: boolean;
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generatedAt?: string | null;
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reason?: string;
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}
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export interface BriefingResponse {
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briefing: Briefing | null;
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context: BriefingContext | null;
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meta: InsightMeta;
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}
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export interface TrendInsight {
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summary: string | null;
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drivers: Array<{ factor: string; detail: string }>;
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caution: string | null;
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confidence: 'high' | 'medium' | 'low';
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}
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export interface TrendInsightResponse {
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insight: TrendInsight | null;
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window: Record<string, any> | null;
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meta: InsightMeta;
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}
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export interface CopilotTurn {
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role: 'user' | 'assistant';
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content: string;
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}
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/**
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* Parse a timestamp the backend wrote with `datetime.utcnow()` — i.e. UTC but
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* with no offset in the string. JavaScript reads such a value as *local* time,
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@@ -675,6 +775,139 @@ class ApiClient {
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);
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return data;
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}
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// --- AI coach ---
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/**
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* 晨间简报 for one day, plus the computed context behind it.
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*
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* Returns immediately. When no stored model answer matches the current data
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* the reply is the rule-based briefing with `meta.pending`, and the model's
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* version is generated in the background — call again to pick it up.
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*/
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async getBriefing(opts: { date?: string; refresh?: boolean; model?: string } = {}) {
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const { data } = await this.client.get<BriefingResponse>('/analysis/briefing', {
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params: {
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date: opts.date,
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model: opts.model,
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...(opts.refresh ? { refresh: 1 } : {}),
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},
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});
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return data;
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}
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/** Attribution for one metric over a selected span. Blocking: a cold
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* generation runs well past axios's default timeout. */
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async getTrendInsight(
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metric: string, startDate: string, endDate: string, refresh?: boolean
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) {
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const { data } = await this.client.get<TrendInsightResponse>(
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'/analysis/trend-insight',
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{
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params: { metric, startDate, endDate, ...(refresh ? { refresh: 1 } : {}) },
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timeout: 300_000,
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}
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);
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return data;
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}
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/**
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* Ask the Copilot, streamed.
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*
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* `fetch` rather than axios or EventSource: axios buffers the whole body
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* before resolving, and EventSource cannot send an Authorization header —
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* which would mean moving the JWT into the query string, where it would be
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* logged by every proxy in the path.
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*
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* `onDelta` is called with each fragment as it arrives. Pass `signal` to
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* abort; the promise resolves with the full text.
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*/
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async streamCopilot(
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question: string,
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opts: {
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history?: CopilotTurn[];
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date?: string;
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model?: string;
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signal?: AbortSignal;
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onDelta?: (text: string) => void;
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} = {}
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): Promise<{ text: string; upstream: string | null }> {
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const token = localStorage.getItem(TOKEN_KEY);
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const resp = await fetch(`${API_BASE_URL}/analysis/copilot`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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...(token ? { Authorization: `Bearer ${token}` } : {}),
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},
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body: JSON.stringify({
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question,
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history: opts.history ?? [],
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date: opts.date,
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model: opts.model,
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}),
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signal: opts.signal,
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});
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if (!resp.ok || !resp.body) {
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let message = `请求失败 (${resp.status})`;
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try {
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message = (await resp.json()).error || message;
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} catch {
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// A non-JSON error body (a proxy's HTML 502) leaves the status text.
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}
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throw new Error(message);
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}
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const reader = resp.body.getReader();
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const decoder = new TextDecoder();
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let buffer = '';
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let text = '';
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let upstream: string | null = null;
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let failure: string | null = null;
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// SSE frames are separated by a blank line and can split across chunks,
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// so the tail of the buffer is kept until its terminator arrives.
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for (;;) {
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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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let split = buffer.indexOf('\n\n');
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while (split !== -1) {
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const frame = buffer.slice(0, split);
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buffer = buffer.slice(split + 2);
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split = buffer.indexOf('\n\n');
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let event = 'message';
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const dataLines: string[] = [];
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for (const line of frame.split('\n')) {
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if (line.startsWith('event:')) event = line.slice(6).trim();
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else if (line.startsWith('data:')) dataLines.push(line.slice(5).trim());
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}
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if (!dataLines.length) continue;
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let payload: any;
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try {
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payload = JSON.parse(dataLines.join('\n'));
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} catch {
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continue;
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}
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if (event === 'delta' && payload.text) {
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text += payload.text;
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opts.onDelta?.(payload.text);
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} else if (event === 'done') {
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upstream = payload.upstream ?? null;
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} else if (event === 'error') {
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// Recorded rather than thrown here: the stream still has to be
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// drained, and the server closes it right after this frame.
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failure = payload.message || '生成失败';
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}
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}
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}
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if (failure) throw new Error(failure);
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return { text, upstream };
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}
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}
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export const apiClient = new ApiClient();
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