forked from lunanting/cangku
432 lines
14 KiB
JavaScript
432 lines
14 KiB
JavaScript
const crypto = require('crypto');
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const planningDatabase = require('./planningDatabase');
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const { ensurePlanningAiImageSchema } = require('./planningSupportSchema');
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const SPACE_ENUM = ['客厅', '卧室', '餐厅', '书房', '办公室', '阳台', '户外', '其他'];
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const SCENE_ENUM = ['家用', '办公', '商用', '酒店民宿', '其他'];
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const STYLE_ENUM = ['现代简约', '中式', '日式', '北欧', '轻奢', '工业风', '复古', '其他'];
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const APPEARANCE_SIMILARITY_CODE_COLUMN = 'appearance_similarity_code';
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function firstText(value) {
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if (value === null || value === undefined) {
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return '';
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}
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return String(value)
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.trim()
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.replace(/[，,]/g, '、')
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.split(/[、/|；;]/)[0]
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.trim();
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}
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function includesAny(text, keywords = []) {
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return keywords.some(keyword => text.includes(keyword));
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}
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function normalizeCategoryTag(value, fallbackCategoryName = null) {
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const text = normalizeText(value || fallbackCategoryName, 100);
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if (!text) {
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return '其他';
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}
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if (includesAny(text, ['边几', '角几', '边桌', '角桌'])) {
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return '边几';
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}
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if (includesAny(text, ['茶几'])) {
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return '茶几';
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}
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if (includesAny(text, ['床头柜'])) {
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return '床头柜';
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}
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if (includesAny(text, ['书桌', '办公桌'])) {
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return '书桌';
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}
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if (includesAny(text, ['餐桌'])) {
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return '餐桌';
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}
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if (includesAny(text, ['椅', '凳'])) {
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return '椅凳';
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}
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if (includesAny(text, ['柜', '收纳'])) {
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return '柜类';
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}
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return firstText(text) || '其他';
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}
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function normalizeSpaceTag(value) {
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const text = normalizeText(value, 100);
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if (!text) {
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return '其他';
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}
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if (includesAny(text, ['客厅', '沙发旁', '会客'])) {
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return '客厅';
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}
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if (includesAny(text, ['卧室', '床边', '床头'])) {
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return '卧室';
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}
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if (includesAny(text, ['餐厅', '餐桌'])) {
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return '餐厅';
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}
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if (includesAny(text, ['书房', '阅读'])) {
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return '书房';
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}
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if (includesAny(text, ['办公室', '办公区', '工位', '会议室'])) {
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return '办公室';
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}
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if (includesAny(text, ['阳台', '露台'])) {
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return '阳台';
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}
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if (includesAny(text, ['户外', '庭院', '花园'])) {
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return '户外';
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}
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return SPACE_ENUM.includes(text) ? text : '其他';
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}
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function normalizeSceneTag(value) {
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const text = normalizeText(value, 100);
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if (!text) {
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return '其他';
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}
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if (includesAny(text, ['家用', '居家', '日常', '家庭'])) {
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return '家用';
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}
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if (includesAny(text, ['办公', '工位', '会议'])) {
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return '办公';
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}
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if (includesAny(text, ['商用', '门店', '展厅', '店铺'])) {
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return '商用';
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}
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if (includesAny(text, ['酒店', '民宿', '客房'])) {
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return '酒店民宿';
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}
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return SCENE_ENUM.includes(text) ? text : '其他';
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}
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function normalizeStyleTag(value) {
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const text = normalizeText(value, 100);
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if (!text) {
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return '其他';
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}
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if (includesAny(text, ['现代简约', '简约现代', '现代'])) {
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return '现代简约';
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}
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if (includesAny(text, ['中式', '新中式'])) {
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return '中式';
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}
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if (includesAny(text, ['日式', '原木风', 'muji'])) {
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return '日式';
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}
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if (includesAny(text, ['北欧'])) {
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return '北欧';
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}
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if (includesAny(text, ['轻奢'])) {
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return '轻奢';
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}
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if (includesAny(text, ['工业风', '工业'])) {
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return '工业风';
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}
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if (includesAny(text, ['复古', '怀旧'])) {
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return '复古';
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}
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return STYLE_ENUM.includes(text) ? text : '其他';
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}
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function normalizeColorTag(value) {
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const text = normalizeText(value, 100);
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if (!text) {
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return '其他';
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}
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if (includesAny(text, ['胡桃', '黑胡桃'])) {
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return '胡桃色';
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}
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if (includesAny(text, ['原木', '浅木', '木纹', '橡木', '榉木'])) {
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return '原木色';
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}
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if (includesAny(text, ['白', '米白', '奶白', '暖白', '象牙白'])) {
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return '白色';
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}
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if (includesAny(text, ['黑', '雅黑'])) {
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return '黑色';
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}
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if (includesAny(text, ['灰', '高级灰'])) {
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return '灰色';
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}
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if (includesAny(text, ['棕', '咖', '栗'])) {
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return '棕色';
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}
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if (includesAny(text, ['金'])) {
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return '金色';
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}
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if (includesAny(text, ['银'])) {
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return '银色';
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}
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return firstText(text) || '其他';
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}
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function normalizeMaterialTag(value) {
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const text = normalizeText(value, 100);
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if (!text) {
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return '其他';
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}
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if (includesAny(text, ['金属', '铁', '钢', '铝', '合金'])) {
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if (includesAny(text, ['木', '板', '密度板', '颗粒板'])) {
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return '金属+木质';
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}
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return '金属';
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}
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if (includesAny(text, ['实木', '木质', '木材', '板材', '密度板', '颗粒板', '木板'])) {
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return '木质';
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}
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if (includesAny(text, ['玻璃'])) {
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return '玻璃';
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}
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if (includesAny(text, ['岩板', '石', '大理石', '石材'])) {
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return '石材';
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}
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if (includesAny(text, ['塑料', 'pp', 'abs', '树脂'])) {
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return '塑料';
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}
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if (includesAny(text, ['布', '皮', '绒'])) {
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return '软包';
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}
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return firstText(text) || '其他';
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}
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function buildNormalizedTags(rawTags = {}, fallback = {}) {
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return {
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category: normalizeCategoryTag(rawTags.category || fallback.category, fallback.categoryName),
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space: normalizeSpaceTag(rawTags.space || fallback.space),
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scene: normalizeSceneTag(rawTags.scene || fallback.scene),
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style: normalizeStyleTag(rawTags.style || fallback.style),
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color: normalizeColorTag(rawTags.color || fallback.color),
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material: normalizeMaterialTag(rawTags.material || fallback.material),
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};
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}
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function safeJsonStringify(value) {
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if (value === null || value === undefined) {
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return null;
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}
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return typeof value === 'string' ? value : JSON.stringify(value);
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}
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function safeJsonParse(value) {
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if (!value) {
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return {};
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}
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if (typeof value === 'object') {
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return value;
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}
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try {
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return JSON.parse(value);
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} catch (error) {
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return {};
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}
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}
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function normalizeText(value, maxLength = null) {
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if (value === null || value === undefined) {
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return null;
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}
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const text = String(value).trim();
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if (!text) {
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return null;
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}
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return maxLength ? text.slice(0, maxLength) : text;
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}
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function normalizeInteger(value) {
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if (value === null || value === undefined || value === '') {
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return null;
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}
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const numeric = Number(value);
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return Number.isFinite(numeric) ? Math.round(numeric) : null;
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}
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function buildImageHash({ imageUrl, sourceSystem, sourceTable, sourceId, skuId, goodsId }) {
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const base = [
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normalizeText(imageUrl) || '',
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normalizeText(sourceSystem) || '',
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normalizeText(sourceTable) || '',
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normalizeText(sourceId) || '',
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normalizeText(skuId) || '',
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normalizeText(goodsId) || '',
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].join('|');
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if (!base.replace(/\|/g, '')) {
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return null;
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}
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return crypto.createHash('sha256').update(base).digest('hex');
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}
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function normalizeFeatureRecord(input = {}) {
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const rawTags = input.rawTags || input.rawTagsJson || {};
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const normalizedTags = buildNormalizedTags(
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safeJsonParse(rawTags),
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{
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category: input.categoryTag,
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space: input.spaceTag,
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scene: input.sceneTag,
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style: input.styleTag,
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color: input.colorTag,
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material: input.materialTag,
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categoryName: input.categoryName,
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}
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);
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const imageHash = normalizeText(input.imageHash, 64)
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|| buildImageHash({
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imageUrl: input.imageUrl,
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sourceSystem: input.sourceSystem,
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sourceTable: input.sourceTable,
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sourceId: input.sourceId,
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skuId: input.skuId,
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goodsId: input.goodsId,
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});
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return {
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biz_type: normalizeText(input.bizType, 30) || 'internal',
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source_system: normalizeText(input.sourceSystem, 50),
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source_table: normalizeText(input.sourceTable, 100),
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source_id: normalizeText(input.sourceId, 100),
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sku_id: normalizeText(input.skuId, 100),
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goods_id: normalizeText(input.goodsId, 100),
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product_name: normalizeText(input.productName, 500),
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category_name: normalizeText(input.categoryName, 200),
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appearance_similarity_code: normalizeText(input.appearanceSimilarityCode || input.appearance_similarity_code, 160),
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image_url: normalizeText(input.imageUrl, 1000),
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image_hash: imageHash,
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image_status: normalizeText(input.imageStatus, 20) || 'success',
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raw_tags_json: safeJsonStringify(rawTags),
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normalized_tags_json: safeJsonStringify(normalizedTags),
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category_tag: normalizeText(normalizedTags.category, 100),
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space_tag: normalizeText(normalizedTags.space, 50),
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scene_tag: normalizeText(normalizedTags.scene, 50),
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style_tag: normalizeText(normalizedTags.style, 100),
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color_tag: normalizeText(normalizedTags.color, 100),
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material_tag: normalizeText(normalizedTags.material, 100),
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vision_provider: normalizeText(input.visionProvider, 50),
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vision_model: normalizeText(input.visionModel, 100),
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vision_prompt_version: normalizeText(input.visionPromptVersion, 50),
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vision_prompt_tokens: normalizeInteger(input.visionPromptTokens),
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vision_completion_tokens: normalizeInteger(input.visionCompletionTokens),
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vision_total_tokens: normalizeInteger(input.visionTotalTokens),
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embedding_provider: normalizeText(input.embeddingProvider, 50),
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embedding_model: normalizeText(input.embeddingModel, 100),
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embedding_vector_json: safeJsonStringify(input.embeddingVector || input.embeddingVectorJson),
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embedding_dimension: normalizeInteger(input.embeddingDimension),
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embedding_prompt_tokens: normalizeInteger(input.embeddingPromptTokens),
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embedding_total_tokens: normalizeInteger(input.embeddingTotalTokens),
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processed_at: input.processedAt ? new Date(input.processedAt) : new Date(),
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error_message: normalizeText(input.errorMessage, 2000),
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};
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}
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async function upsertAiImageFeature(record) {
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await ensurePlanningAiImageSchema();
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const payload = normalizeFeatureRecord(record);
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if (!payload.image_url) {
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throw new Error('imageUrl 不能为空');
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}
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if (!payload.source_id && !payload.image_hash) {
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throw new Error('sourceId 和 imageHash 至少需要一个');
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}
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const sql = `
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INSERT INTO ai_image_feature_library (
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biz_type, source_system, source_table, source_id, sku_id, goods_id, product_name, category_name,
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appearance_similarity_code,
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image_url, image_hash, image_status, raw_tags_json, normalized_tags_json,
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category_tag, space_tag, scene_tag, style_tag, color_tag, material_tag,
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vision_provider, vision_model, vision_prompt_version, vision_prompt_tokens, vision_completion_tokens, vision_total_tokens,
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embedding_provider, embedding_model, embedding_vector_json, embedding_dimension, embedding_prompt_tokens, embedding_total_tokens,
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processed_at, error_message
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) VALUES (
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?, ?, ?, ?, ?, ?, ?, ?,
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?,
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?, ?, ?, ?, ?,
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?, ?, ?, ?, ?, ?,
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?, ?, ?, ?, ?, ?,
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?, ?, ?, ?, ?, ?,
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?, ?
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)
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ON DUPLICATE KEY UPDATE
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sku_id = VALUES(sku_id),
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goods_id = VALUES(goods_id),
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product_name = VALUES(product_name),
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category_name = VALUES(category_name),
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appearance_similarity_code = COALESCE(VALUES(appearance_similarity_code), appearance_similarity_code),
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image_url = VALUES(image_url),
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image_status = VALUES(image_status),
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raw_tags_json = VALUES(raw_tags_json),
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normalized_tags_json = VALUES(normalized_tags_json),
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category_tag = VALUES(category_tag),
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space_tag = VALUES(space_tag),
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scene_tag = VALUES(scene_tag),
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style_tag = VALUES(style_tag),
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color_tag = VALUES(color_tag),
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material_tag = VALUES(material_tag),
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vision_provider = VALUES(vision_provider),
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vision_model = VALUES(vision_model),
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vision_prompt_version = VALUES(vision_prompt_version),
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vision_prompt_tokens = VALUES(vision_prompt_tokens),
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vision_completion_tokens = VALUES(vision_completion_tokens),
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vision_total_tokens = VALUES(vision_total_tokens),
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embedding_provider = VALUES(embedding_provider),
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embedding_model = VALUES(embedding_model),
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embedding_vector_json = VALUES(embedding_vector_json),
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embedding_dimension = VALUES(embedding_dimension),
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embedding_prompt_tokens = VALUES(embedding_prompt_tokens),
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embedding_total_tokens = VALUES(embedding_total_tokens),
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processed_at = VALUES(processed_at),
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error_message = VALUES(error_message),
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updated_at = CURRENT_TIMESTAMP
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`;
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const params = [
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payload.biz_type, payload.source_system, payload.source_table, payload.source_id, payload.sku_id, payload.goods_id, payload.product_name, payload.category_name,
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payload.appearance_similarity_code,
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payload.image_url, payload.image_hash, payload.image_status, payload.raw_tags_json, payload.normalized_tags_json,
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payload.category_tag, payload.space_tag, payload.scene_tag, payload.style_tag, payload.color_tag, payload.material_tag,
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payload.vision_provider, payload.vision_model, payload.vision_prompt_version, payload.vision_prompt_tokens, payload.vision_completion_tokens, payload.vision_total_tokens,
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payload.embedding_provider, payload.embedding_model, payload.embedding_vector_json, payload.embedding_dimension, payload.embedding_prompt_tokens, payload.embedding_total_tokens,
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payload.processed_at, payload.error_message,
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];
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const result = await planningDatabase.execute(sql, params);
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return {
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affectedRows: result?.affectedRows || 0,
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imageHash: payload.image_hash,
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sourceId: payload.source_id,
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};
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}
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async function batchUpsertAiImageFeatures(records = []) {
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const results = [];
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for (const record of records) {
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results.push(await upsertAiImageFeature(record));
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}
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return results;
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}
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module.exports = {
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APPEARANCE_SIMILARITY_CODE_COLUMN,
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buildNormalizedTags,
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buildImageHash,
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normalizeFeatureRecord,
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upsertAiImageFeature,
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batchUpsertAiImageFeatures,
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};
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