forked from wangziqi/gongxue-base
feat: add AI school recommendation foundation
This commit is contained in:
Codex
@@ -1,5 +1,6 @@
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import type { Handler } from './http.js';
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import { routeKey } from './http.js';
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import { aiRoutes } from '../features/ai/index.js';
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import { authRoutes } from '../features/auth/index.js';
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import { catalogRoutes } from '../features/catalog/index.js';
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import { commerceRoutes } from '../features/commerce/index.js';
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@@ -31,6 +32,7 @@ const allRoutes: RouteDefinition[] = [
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...healthRoutes,
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...authRoutes,
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...tenantRoutes,
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...aiRoutes,
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...catalogRoutes,
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...learningRoutes,
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...profileRoutes,
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12
apps/api/src/features/ai/index.ts
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12
apps/api/src/features/ai/index.ts
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@@ -0,0 +1,12 @@
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import type { RouteDefinition } from '../../core/router.js';
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import {
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generateSchoolRecommendationRoute,
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schoolRecommendationReportDetailRoute,
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schoolRecommendationReportsRoute,
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} from './routes.js';
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export const aiRoutes: RouteDefinition[] = [
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['GET', '/api/ai/school-recommendations', schoolRecommendationReportsRoute],
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['GET', '/api/ai/school-recommendations/detail', schoolRecommendationReportDetailRoute],
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['POST', '/api/ai/school-recommendations/generate', generateSchoolRecommendationRoute],
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];
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546
apps/api/src/features/ai/routes.ts
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546
apps/api/src/features/ai/routes.ts
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@@ -0,0 +1,546 @@
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import { HttpError, type RequestContext } from '../../core/http.js';
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import { intParam, readJsonBody, stringParam, tenantIdFrom, userIdFrom } from '../../core/request.js';
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import { query, queryOne, transaction } from '../../core/db.js';
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type JsonObject = Record<string, unknown>;
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interface StudentProfileContext {
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regionId: string | null;
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regionName: string | null;
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selectedSchoolId: string | null;
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selectedSchoolName: string | null;
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selectedMajorId: string | null;
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selectedMajorName: string | null;
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stats: JsonObject;
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}
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interface RegionRow {
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id: string;
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name: string;
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code: string | null;
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}
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interface EntitlementRow {
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id: string;
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scopeType: string;
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scopeId: string | null;
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expiresAt: string | null;
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}
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interface ScorelineFieldRow {
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fieldKey: string;
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fieldName: string;
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fieldType: string | null;
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unit: string | null;
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isTrend: boolean;
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sortOrder: number;
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}
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interface ScorelineRecordRow {
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id: string;
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year: number;
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schoolId: string | null;
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schoolName: string | null;
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majorId: string | null;
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majorName: string | null;
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fieldValues: JsonObject;
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}
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interface RecommendationCandidate {
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schoolId: string | null;
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schoolName: string;
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majorId: string | null;
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majorName: string | null;
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latestYear: number | null;
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latestScore: number | null;
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averageScore: number | null;
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scoreGap: number | null;
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riskLevel: 'safe' | 'balanced' | 'sprint' | 'unknown';
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confidence: number;
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reason: string;
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scorelineTrend: {
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years: number[];
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scores: (number | null)[];
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direction: 'up' | 'down' | 'flat' | 'unknown';
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};
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tags: string[];
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}
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const RISK_PREFERENCES = new Set(['safe', 'balanced', 'sprint']);
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const PROMPT_VERSION = 'school-recommendation-v1';
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const LOCAL_MODEL = 'local-scoreline-rules-v1';
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const DISCLAIMER = [
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'推荐结果仅用于择校和备考规划参考，不构成录取承诺。',
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'分数线、招生计划和考试政策可能变化，正式报考前应以院校和考试院官方信息为准。',
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'当地区或院校数据覆盖不足时，应结合人工咨询和最新招生简章复核。',
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];
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function objectValue(value: unknown): JsonObject {
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return value && typeof value === 'object' && !Array.isArray(value) ? value as JsonObject : {};
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}
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function nullableString(value: unknown) {
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return typeof value === 'string' && value.trim() ? value.trim() : null;
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}
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function boundedNumber(value: unknown, min: number, max: number) {
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const parsed = Number(value);
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if (!Number.isFinite(parsed)) return null;
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return Math.min(Math.max(parsed, min), max);
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}
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function boundedText(value: unknown, maxLength: number) {
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const text = nullableString(value);
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if (!text) return null;
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return text.slice(0, maxLength);
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}
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function normalizeRiskPreference(value: unknown) {
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const riskPreference = nullableString(value) || 'balanced';
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if (!RISK_PREFERENCES.has(riskPreference)) {
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throw new HttpError(400, 'riskPreference must be safe, balanced, or sprint', 'INVALID_AI_RISK_PREFERENCE');
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}
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return riskPreference as 'safe' | 'balanced' | 'sprint';
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}
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function numericField(value: unknown) {
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const parsed = Number(value);
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return Number.isFinite(parsed) ? parsed : null;
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}
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function pickScoreValue(values: JsonObject) {
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const preferredKeys = [
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'minScore',
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'minimumScore',
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'score',
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'admissionScore',
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'lowestScore',
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'投档线',
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'最低分',
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];
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for (const key of preferredKeys) {
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const score = numericField(values[key]);
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if (score !== null) return score;
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}
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for (const [key, value] of Object.entries(values)) {
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if (/score|分|线/i.test(key)) {
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const score = numericField(value);
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if (score !== null) return score;
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}
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}
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return null;
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}
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function riskFromGap(gap: number | null, preference: 'safe' | 'balanced' | 'sprint') {
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if (gap === null) return 'unknown';
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const safeFloor = preference === 'safe' ? 18 : preference === 'sprint' ? 8 : 12;
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const sprintFloor = preference === 'safe' ? -2 : preference === 'sprint' ? -15 : -8;
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if (gap >= safeFloor) return 'safe';
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if (gap >= sprintFloor) return 'balanced';
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return 'sprint';
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}
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function confidenceFromGap(gap: number | null, recordsCount: number, riskLevel: string) {
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if (gap === null) return recordsCount > 1 ? 0.45 : 0.35;
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const base = riskLevel === 'safe' ? 0.78 : riskLevel === 'balanced' ? 0.62 : 0.42;
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const gapBonus = Math.min(Math.abs(gap) / 100, 0.12);
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const coverageBonus = Math.min(recordsCount * 0.025, 0.1);
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return Number(Math.min(base + gapBonus + coverageBonus, 0.95).toFixed(2));
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}
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function trendDirection(scores: (number | null)[]) {
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const numericScores = scores.filter((score): score is number => typeof score === 'number');
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if (numericScores.length < 2) return 'unknown';
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const first = numericScores[0];
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const last = numericScores[numericScores.length - 1];
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const diff = last - first;
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if (Math.abs(diff) <= 3) return 'flat';
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return diff > 0 ? 'up' : 'down';
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}
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function groupKey(record: ScorelineRecordRow) {
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return `${record.schoolId || record.schoolName || 'unknown'}:${record.majorId || record.majorName || 'unknown'}`;
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}
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function buildCandidates(
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records: ScorelineRecordRow[],
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input: ReturnType<typeof normalizeRecommendationInput>,
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) {
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const groups = new Map<string, ScorelineRecordRow[]>();
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for (const record of records) {
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const key = groupKey(record);
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groups.set(key, [...(groups.get(key) || []), record]);
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}
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const candidates = [...groups.values()].map(group => {
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const sorted = group.slice().sort((left, right) => left.year - right.year);
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const latest = sorted.at(-1) || null;
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const scores = sorted.map(record => pickScoreValue(record.fieldValues));
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const numericScores = scores.filter((score): score is number => typeof score === 'number');
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const latestScore = latest ? pickScoreValue(latest.fieldValues) : null;
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const averageScore = numericScores.length
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? Number((numericScores.reduce((sum, score) => sum + score, 0) / numericScores.length).toFixed(1))
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: null;
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const baseline = latestScore ?? averageScore;
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const scoreGap = baseline === null || input.estimatedScore === null
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? null
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: Number((input.estimatedScore - baseline).toFixed(1));
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const riskLevel = riskFromGap(scoreGap, input.riskPreference);
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const direction = trendDirection(scores);
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const confidence = confidenceFromGap(scoreGap, sorted.length, riskLevel);
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const tags = [
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riskLevel === 'safe' ? '稳妥' : riskLevel === 'balanced' ? '匹配' : riskLevel === 'sprint' ? '冲刺' : '数据不足',
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direction === 'up' ? '分数线上升' : direction === 'down' ? '分数线下降' : direction === 'flat' ? '分数线稳定' : '趋势不足',
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sorted.length >= 3 ? '多年数据' : '样本较少',
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];
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const schoolName = latest?.schoolName || group[0]?.schoolName || '未知院校';
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const majorName = latest?.majorName || group[0]?.majorName || null;
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const reasonParts = [
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input.estimatedScore === null
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? '未提供预估分，按历年分数线和数据覆盖度排序。'
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: `预估分与最新参考线差值约 ${scoreGap ?? '未知'} 分。`,
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direction === 'up'
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? '近年参考线有上升趋势，建议预留安全分差。'
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: direction === 'down'
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? '近年参考线略有下降，可作为匹配或冲刺备选。'
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: direction === 'flat'
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? '近年参考线相对稳定，适合纳入重点比较。'
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: '历史数据不足，建议结合招生计划复核。',
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];
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return {
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schoolId: latest?.schoolId || group[0]?.schoolId || null,
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schoolName,
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majorId: latest?.majorId || group[0]?.majorId || null,
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majorName,
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latestYear: latest?.year || null,
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latestScore,
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averageScore,
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scoreGap,
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riskLevel,
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confidence,
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reason: reasonParts.join(' '),
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scorelineTrend: {
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years: sorted.map(record => record.year),
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scores,
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direction,
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},
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tags,
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} satisfies RecommendationCandidate;
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});
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const riskOrder = {
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safe: input.riskPreference === 'safe' ? 0 : 1,
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balanced: input.riskPreference === 'balanced' ? 0 : 2,
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sprint: input.riskPreference === 'sprint' ? 0 : 3,
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unknown: 4,
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};
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return candidates
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.sort((left, right) => {
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const riskDiff = riskOrder[left.riskLevel] - riskOrder[right.riskLevel];
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if (riskDiff !== 0) return riskDiff;
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if (right.confidence !== left.confidence) return right.confidence - left.confidence;
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return (right.latestYear || 0) - (left.latestYear || 0);
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})
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.slice(0, input.recommendationLimit);
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}
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function normalizeRecommendationInput(body: JsonObject) {
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const estimatedScore = boundedNumber(body.estimatedScore, 0, 1000);
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const recommendationLimit = Math.trunc(boundedNumber(body.recommendationLimit, 1, 12) || 5);
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return {
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regionId: nullableString(body.regionId),
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estimatedScore,
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examTrack: boundedText(body.examTrack, 80),
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preferredCity: boundedText(body.preferredCity, 80),
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targetSchoolId: nullableString(body.targetSchoolId),
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targetMajorId: nullableString(body.targetMajorId),
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riskPreference: normalizeRiskPreference(body.riskPreference),
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constraints: boundedText(body.constraints, 500),
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notes: boundedText(body.notes, 500),
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recommendationLimit,
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};
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}
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async function loadStudentProfile(tenantId: string, userId: string) {
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return queryOne<StudentProfileContext>(
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`
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select sp.region_id as "regionId", r.name as "regionName",
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sp.selected_school_id as "selectedSchoolId", s.name as "selectedSchoolName",
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sp.selected_major_id as "selectedMajorId", m.name as "selectedMajorName",
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sp.stats
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from public.student_profiles sp
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left join public.regions r on r.id = sp.region_id and r.tenant_id = sp.tenant_id
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left join public.schools s on s.id = sp.selected_school_id and s.tenant_id = sp.tenant_id
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left join public.majors m on m.id = sp.selected_major_id and m.tenant_id = sp.tenant_id
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where sp.tenant_id = $1 and sp.user_id = $2
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limit 1
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`,
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[tenantId, userId],
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);
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}
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async function assertRegionAccess(tenantId: string, regionId: string) {
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const region = await queryOne<RegionRow>(
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`
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select id, name, code
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from public.regions
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where tenant_id = $1 and id = $2 and is_active = true
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limit 1
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`,
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[tenantId, regionId],
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);
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if (!region) throw new HttpError(404, 'Region not found for this tenant', 'AI_REGION_NOT_FOUND');
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return region;
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}
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async function activeSvipEntitlement(tenantId: string, userId: string, regionId: string | null) {
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const now = new Date().toISOString();
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return queryOne<EntitlementRow>(
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`
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select id, scope_type as "scopeType", scope_id as "scopeId", expires_at as "expiresAt"
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from public.entitlements
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where tenant_id = $1
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and user_id = $2
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and entitlement_type = 'svip'
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and status = 'active'
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and starts_at <= $3::timestamptz
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and (expires_at is null or expires_at > $3::timestamptz)
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and (
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scope_type = 'tenant'
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or ($4::uuid is not null and scope_type = 'region' and scope_id = $4::uuid)
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)
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order by case when scope_type = 'region' then 0 else 1 end, expires_at desc nulls first
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limit 1
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`,
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[tenantId, userId, now, regionId],
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);
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}
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async function loadScorelineFields(tenantId: string, regionId: string) {
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return query<ScorelineFieldRow>(
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`
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select field_key as "fieldKey", field_name as "fieldName",
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field_type as "fieldType", unit, is_trend as "isTrend",
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sort_order as "sortOrder"
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from public.scoreline_fields
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where tenant_id = $1
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and (region_id is null or region_id = $2::uuid)
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and is_visible = true
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order by sort_order asc, field_name asc
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`,
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[tenantId, regionId],
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);
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}
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async function loadScorelineRecords(tenantId: string, regionId: string, input: ReturnType<typeof normalizeRecommendationInput>) {
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const params: unknown[] = [tenantId, regionId, input.targetSchoolId, input.targetMajorId, 180];
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return query<ScorelineRecordRow>(
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`
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select id, year, school_id as "schoolId", school_name as "schoolName",
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major_id as "majorId", major_name as "majorName",
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field_values as "fieldValues"
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from public.scoreline_records
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where tenant_id = $1
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and region_id = $2::uuid
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and ($3::uuid is null or school_id = $3::uuid)
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and ($4::uuid is null or major_id = $4::uuid)
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order by year desc, school_name asc nulls last, major_name asc nulls last
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limit $5
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`,
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params,
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);
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}
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function buildReportResult(input: ReturnType<typeof normalizeRecommendationInput>, context: JsonObject, candidates: RecommendationCandidate[]) {
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const region = objectValue(context.region);
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const dataCoverage = objectValue(context.dataCoverage);
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const safeCount = candidates.filter(candidate => candidate.riskLevel === 'safe').length;
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const balancedCount = candidates.filter(candidate => candidate.riskLevel === 'balanced').length;
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const sprintCount = candidates.filter(candidate => candidate.riskLevel === 'sprint').length;
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const top = candidates[0] || null;
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const riskLevel =
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safeCount >= 2 ? 'safe' :
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balancedCount >= 2 || top?.riskLevel === 'balanced' ? 'balanced' :
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sprintCount ? 'sprint' : 'unknown';
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return {
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schemaVersion: 'school-recommendation-report-v1',
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summary: top
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? `基于当前地区历年分数线，优先推荐 ${top.schoolName}${top.majorName ? `-${top.majorName}` : ''} 等 ${candidates.length} 个方案。`
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: '当前地区分数线数据不足，暂无法生成可靠院校推荐。',
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riskLevel,
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recommendedSchools: candidates,
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actionPlan: [
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'先确认目标地区、考试类别和预估分是否准确。',
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'重点比较推荐院校近三年分数线、招生计划和专业限制。',
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'把稳妥、匹配、冲刺院校分别保留 2-3 个备选，并跟进最新招生简章。',
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input.constraints ? '结合个人限制条件逐项排除不符合报考条件的院校或专业。' : '补充个人限制条件后可再次生成更精细的推荐。',
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],
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disclaimers: DISCLAIMER,
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dataCoverage: {
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regionId: region.id || null,
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regionName: region.name || null,
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scorelineRecordCount: dataCoverage.scorelineRecordCount || 0,
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schoolMajorGroupCount: candidates.length,
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years: dataCoverage.years || [],
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fields: dataCoverage.fields || [],
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||||
provider: 'local_rules',
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
function reportSelectSql() {
|
||||
return `
|
||||
select id, tenant_id as "tenantId", user_id as "userId", region_id as "regionId",
|
||||
status, provider, model, prompt_version as "promptVersion",
|
||||
input_payload as "inputPayload", context_payload as "contextPayload",
|
||||
result_payload as "resultPayload", error_message as "errorMessage",
|
||||
generated_at as "generatedAt", created_at as "createdAt", updated_at as "updatedAt"
|
||||
from public.ai_recommendation_reports
|
||||
`;
|
||||
}
|
||||
|
||||
export async function generateSchoolRecommendationRoute(ctx: RequestContext) {
|
||||
const body = await readJsonBody(ctx);
|
||||
const tenantId = await tenantIdFrom(ctx);
|
||||
const userId = await userIdFrom(ctx, body);
|
||||
const input = normalizeRecommendationInput(body);
|
||||
const profile = await loadStudentProfile(tenantId, userId);
|
||||
if (!profile) throw new HttpError(404, 'Student profile not found', 'PROFILE_NOT_FOUND');
|
||||
|
||||
const regionId = input.regionId || profile.regionId;
|
||||
if (!regionId) throw new HttpError(400, 'regionId is required before generating a recommendation', 'AI_REGION_REQUIRED');
|
||||
const region = await assertRegionAccess(tenantId, regionId);
|
||||
const entitlement = await activeSvipEntitlement(tenantId, userId, regionId);
|
||||
if (!entitlement) {
|
||||
throw new HttpError(403, 'SVIP entitlement is required for AI school recommendation', 'AI_SVIP_REQUIRED');
|
||||
}
|
||||
|
||||
const [fields, records] = await Promise.all([
|
||||
loadScorelineFields(tenantId, regionId),
|
||||
loadScorelineRecords(tenantId, regionId, input),
|
||||
]);
|
||||
const years = [...new Set(records.map(record => record.year))].sort((left, right) => right - left);
|
||||
const candidates = buildCandidates(records, input);
|
||||
const contextPayload = {
|
||||
region,
|
||||
studentProfile: {
|
||||
regionId: profile.regionId,
|
||||
regionName: profile.regionName,
|
||||
selectedSchoolId: profile.selectedSchoolId,
|
||||
selectedSchoolName: profile.selectedSchoolName,
|
||||
selectedMajorId: profile.selectedMajorId,
|
||||
selectedMajorName: profile.selectedMajorName,
|
||||
},
|
||||
entitlement: {
|
||||
id: entitlement.id,
|
||||
scopeType: entitlement.scopeType,
|
||||
scopeId: entitlement.scopeId,
|
||||
expiresAt: entitlement.expiresAt,
|
||||
},
|
||||
dataCoverage: {
|
||||
scorelineRecordCount: records.length,
|
||||
years,
|
||||
fields: fields.map(field => ({
|
||||
fieldKey: field.fieldKey,
|
||||
fieldName: field.fieldName,
|
||||
fieldType: field.fieldType,
|
||||
unit: field.unit,
|
||||
isTrend: field.isTrend,
|
||||
})),
|
||||
},
|
||||
};
|
||||
const resultPayload = buildReportResult(input, contextPayload, candidates);
|
||||
|
||||
const item = await transaction(async client => {
|
||||
const result = await client.query(
|
||||
`
|
||||
insert into public.ai_recommendation_reports (
|
||||
tenant_id, user_id, region_id, status, provider, model, prompt_version,
|
||||
input_payload, context_payload, result_payload, generated_at
|
||||
)
|
||||
values (
|
||||
$1, $2, $3::uuid, 'generated', 'local_rules', $4, $5,
|
||||
$6::jsonb, $7::jsonb, $8::jsonb, now()
|
||||
)
|
||||
returning id, tenant_id as "tenantId", user_id as "userId", region_id as "regionId",
|
||||
status, provider, model, prompt_version as "promptVersion",
|
||||
input_payload as "inputPayload", context_payload as "contextPayload",
|
||||
result_payload as "resultPayload", error_message as "errorMessage",
|
||||
generated_at as "generatedAt", created_at as "createdAt", updated_at as "updatedAt"
|
||||
`,
|
||||
[
|
||||
tenantId,
|
||||
userId,
|
||||
regionId,
|
||||
LOCAL_MODEL,
|
||||
PROMPT_VERSION,
|
||||
JSON.stringify(input),
|
||||
JSON.stringify(contextPayload),
|
||||
JSON.stringify(resultPayload),
|
||||
],
|
||||
);
|
||||
await client.query(
|
||||
`
|
||||
insert into public.audit_logs (tenant_id, actor_user_id, action, target_type, target_id, details)
|
||||
values ($1, $2, 'ai.school_recommendation.generated', 'ai_recommendation_report', $3, $4::jsonb)
|
||||
`,
|
||||
[
|
||||
tenantId,
|
||||
userId,
|
||||
result.rows[0].id,
|
||||
JSON.stringify({
|
||||
provider: 'local_rules',
|
||||
model: LOCAL_MODEL,
|
||||
promptVersion: PROMPT_VERSION,
|
||||
regionId,
|
||||
scorelineRecordCount: records.length,
|
||||
recommendationCount: candidates.length,
|
||||
}),
|
||||
],
|
||||
);
|
||||
return result.rows[0];
|
||||
});
|
||||
|
||||
return { item };
|
||||
}
|
||||
|
||||
export async function schoolRecommendationReportsRoute(ctx: RequestContext) {
|
||||
const tenantId = await tenantIdFrom(ctx);
|
||||
const userId = await userIdFrom(ctx);
|
||||
const limit = intParam(ctx, 'limit', 20, 100);
|
||||
const regionId = stringParam(ctx, 'regionId');
|
||||
|
||||
const items = await query(
|
||||
`
|
||||
${reportSelectSql()}
|
||||
where tenant_id = $1
|
||||
and user_id = $2
|
||||
and ($3::uuid is null or region_id = $3::uuid)
|
||||
order by created_at desc
|
||||
limit $4
|
||||
`,
|
||||
[tenantId, userId, regionId || null, limit],
|
||||
);
|
||||
return { items };
|
||||
}
|
||||
|
||||
export async function schoolRecommendationReportDetailRoute(ctx: RequestContext) {
|
||||
const tenantId = await tenantIdFrom(ctx);
|
||||
const userId = await userIdFrom(ctx);
|
||||
const reportId = stringParam(ctx, 'reportId');
|
||||
if (!reportId) throw new HttpError(400, 'reportId is required', 'AI_REPORT_ID_REQUIRED');
|
||||
|
||||
const item = await queryOne(
|
||||
`
|
||||
${reportSelectSql()}
|
||||
where tenant_id = $1 and user_id = $2 and id = $3::uuid
|
||||
limit 1
|
||||
`,
|
||||
[tenantId, userId, reportId],
|
||||
);
|
||||
if (!item) throw new HttpError(404, 'AI recommendation report not found', 'AI_REPORT_NOT_FOUND');
|
||||
return { item };
|
||||
}
|
||||
Reference in New Issue
Block a user