forked from lunanting/cangku
256 lines
11 KiB
JavaScript
256 lines
11 KiB
JavaScript
'use strict';
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const test = require('node:test');
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const assert = require('node:assert/strict');
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const {
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QWEN_PROVIDER,
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AGNES_PROVIDER,
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DEFAULT_QWEN_BASE_URL,
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DEFAULT_QWEN_MODEL,
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DEFAULT_AGNES_BASE_URL,
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DEFAULT_AGNES_MODEL,
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_test: {
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resolveAiConfig,
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buildAiRequestPayload,
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parseModelJson,
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mapChatDataMeta,
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aggregateChatDimensionRows,
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createChatBatches,
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normalizeAnalysis,
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mergeBatchAnalysesLocally,
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reconcileMergedAnalysis,
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sumUsages,
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getAiErrorDetail,
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isRetryableAiError,
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getAiRetryDelayMs,
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sanitizePublicError,
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},
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} = require('../src/services/csQualityService');
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test('chat data metadata exposes the actual available date range', () => {
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assert.deepEqual(mapChatDataMeta({
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minDate: '2026-06-22',
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latestDate: '2026-07-31 23:59:59',
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messageCount: '792771',
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}), {
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tableName: 'rpt.pdd_GoodsChatMessages',
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minDate: '2026-06-22',
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latestDate: '2026-07-31',
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messageCount: 792771,
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});
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});
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function message(uid, content, index = 1) {
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return {
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uid,
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nickname: `买家-${uid}`,
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msgId: `${uid}-${index}`,
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msgTime: `2026-07-30 10:00:${String(index).padStart(2, '0')}`,
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directionLabel: '买家',
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csname: '客服A',
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content,
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};
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}
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function result(overrides = {}) {
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return {
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consultation_type: '售前',
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primary_reason: '产品问题',
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secondary_reason: '尺寸问题',
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occurrence_count: 1,
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affected_customer_count: 1,
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affected_conversation_ids: ['u1'],
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buyer_intent: '咨询尺寸',
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evidence: [{ conversation_id: 'u1', nickname: '买家-u1', message_time: '2026-07-30 10:00:01', text: '多高' }],
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image_urls: [],
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product_improvement_areas: ['详情页'],
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improvement_suggestion: '补充尺寸图',
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confidence: 0.9,
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...overrides,
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};
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}
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test('customer-service quality defaults to the verified Qwen model and endpoint', () => {
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const config = resolveAiConfig({ apiKey: 'test-key' });
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assert.equal(QWEN_PROVIDER, 'aliyun-model-studio');
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assert.equal(config.provider, QWEN_PROVIDER);
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assert.equal(config.baseUrl, DEFAULT_QWEN_BASE_URL);
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assert.equal(config.model, DEFAULT_QWEN_MODEL);
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assert.equal(config.batchConcurrency, 2);
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assert.equal(config.batchMaxChars, 45000);
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assert.equal(config.mergeMaxBatches, 8);
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assert.equal(config.requestRetryCount, 2);
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assert.equal(config.retryBaseDelayMs, 1500);
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});
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test('customer-service quality resolves the Agnes China endpoint and request format', () => {
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const config = resolveAiConfig({
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provider: AGNES_PROVIDER,
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apiKey: 'test-key',
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});
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assert.equal(config.provider, AGNES_PROVIDER);
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assert.equal(config.baseUrl, DEFAULT_AGNES_BASE_URL);
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assert.equal(config.model, DEFAULT_AGNES_MODEL);
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assert.equal(config.timeoutMs, 600000);
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const payload = buildAiRequestPayload(config, [{ role: 'user', content: 'test' }], 2048);
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assert.equal(payload.model, DEFAULT_AGNES_MODEL);
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assert.equal(payload.max_tokens, 2048);
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assert.equal(payload.temperature, 0);
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assert.equal(payload.stream, false);
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assert.equal('max_completion_tokens' in payload, false);
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assert.equal('response_format' in payload, false);
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});
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test('Qwen requests retain the provider-specific JSON parameters', () => {
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const config = resolveAiConfig({ apiKey: 'test-key' });
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const payload = buildAiRequestPayload(config, [{ role: 'user', content: 'test' }], 2048);
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assert.equal(payload.max_completion_tokens, 2048);
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assert.deepEqual(payload.response_format, { type: 'json_object' });
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assert.equal(payload.enable_thinking, false);
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assert.equal('max_tokens' in payload, false);
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});
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test('chat batching keeps every customer conversation intact', () => {
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const rows = [
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message('u1', '甲'.repeat(700), 1),
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message('u1', '乙'.repeat(700), 2),
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message('u2', '丙'.repeat(700), 1),
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message('u3', '丁'.repeat(700), 1),
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];
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const batches = createChatBatches(rows, 1300);
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assert.equal(batches.length, 3);
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assert.deepEqual(batches[0].conversationIds, ['u1']);
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assert.equal(batches[0].messageCount, 2);
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assert.ok(batches.every((batch) => !(
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batch.conversationIds.includes('u1') && batch.conversationIds.length > 1
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)));
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assert.deepEqual(batches.flatMap((batch) => batch.conversationIds), ['u1', 'u2', 'u3']);
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assert.equal(batches[0].startDate, '2026-07-30');
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assert.equal(batches[0].endDate, '2026-07-30');
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});
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test('chat dimension snapshot derives goods and customers in one aggregation', () => {
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const snapshot = aggregateChatDimensionRows([
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{
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goodsId: 'g1', uid: 'u1', csname: '客服A', goodsName: '商品1', shopName: '店铺1',
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nickname: '客户1', messageCount: 2, firstMsgTime: '2026-07-29T08:00:00.000Z', lastMsgTime: '2026-07-29T08:01:00.000Z',
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},
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{
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goodsId: 'g1', uid: 'u1', csname: '客服B', goodsName: '商品1', shopName: '店铺1',
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nickname: '客户1', messageCount: 3, firstMsgTime: '2026-07-29T08:02:00.000Z', lastMsgTime: '2026-07-29T08:04:00.000Z',
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},
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{
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goodsId: 'g1', uid: 'u2', csname: '客服A', goodsName: '商品1', shopName: '店铺1',
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nickname: '客户2', messageCount: 1, firstMsgTime: '2026-07-29T09:00:00.000Z', lastMsgTime: '2026-07-29T09:00:00.000Z',
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},
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{
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goodsId: 'g2', uid: 'u3', csname: '客服C', goodsName: '商品2', shopName: '店铺2',
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nickname: '客户3', messageCount: 4, firstMsgTime: '2026-07-29T10:00:00.000Z', lastMsgTime: '2026-07-29T10:03:00.000Z',
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},
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]);
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assert.equal(snapshot.goodsRows.length, 2);
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assert.equal(snapshot.goodsRows[0].goodsId, 'g1');
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assert.equal(snapshot.goodsRows[0].messageCount, 6);
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assert.equal(snapshot.goodsRows[0].userCount, 2);
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assert.equal(snapshot.goodsRows[0].csCount, 2);
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assert.equal(snapshot.customersByGoods.g1.length, 2);
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assert.equal(snapshot.customersByGoods.g1[0].uid, 'u1');
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assert.equal(snapshot.customersByGoods.g1[0].messageCount, 5);
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assert.equal(snapshot.customersByGoods.g1[0].csname, '客服B');
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assert.equal(snapshot.customersByGoods.g1[0].firstMsgTime, '2026-07-29T08:00:00.000Z');
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assert.equal(snapshot.customersByGoods.g1[0].lastMsgTime, '2026-07-29T08:04:00.000Z');
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});
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test('local merge sums disjoint batch counts and deduplicates evidence', () => {
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const analyses = [
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{
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summary: { pre_sale_conversation_count: 1, after_sale_conversation_count: 0, mixed_conversation_count: 0, main_findings: ['尺寸咨询较多'] },
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results: [result()],
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},
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{
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summary: { pre_sale_conversation_count: 2, after_sale_conversation_count: 0, mixed_conversation_count: 0, main_findings: ['尺寸咨询较多'] },
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results: [result({
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occurrence_count: 3,
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affected_customer_count: 2,
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affected_conversation_ids: ['u1', 'u2'],
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evidence: [
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{ conversation_id: 'u1', nickname: '买家-u1', message_time: '2026-07-30 10:00:01', text: '多高' },
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{ conversation_id: 'u2', nickname: '买家-u2', message_time: '2026-07-30 10:00:02', text: '多宽' },
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],
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})],
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},
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];
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const merged = mergeBatchAnalysesLocally(analyses, { customerCount: 3, messageCount: 12 });
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assert.equal(merged.results.length, 1);
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assert.equal(merged.results[0].occurrence_count, 4);
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assert.equal(merged.results[0].affected_customer_count, 3);
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assert.deepEqual(merged.results[0].affected_conversation_ids, ['u1', 'u2']);
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assert.equal(merged.results[0].evidence.length, 2);
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assert.equal(merged.summary.conversation_count, 3);
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assert.equal(merged.summary.pre_sale_conversation_count, 3);
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assert.deepEqual(merged.summary.main_findings, ['尺寸咨询较多']);
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});
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test('model merge cannot change deterministic counts or drop a label group', () => {
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const local = mergeBatchAnalysesLocally([{
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summary: { pre_sale_conversation_count: 2, after_sale_conversation_count: 0, mixed_conversation_count: 0, main_findings: [] },
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results: [result({ occurrence_count: 4, affected_customer_count: 2 })],
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}], { customerCount: 2, messageCount: 8 });
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const model = {
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schema_version: '1.0',
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conversation_type: '售前',
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summary: { conversation_count: 99, pre_sale_conversation_count: 99, after_sale_conversation_count: 0, mixed_conversation_count: 0, main_findings: ['优化后的结论'] },
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results: [result({ occurrence_count: 99, affected_customer_count: 99, buyer_intent: '优化后的诉求' })],
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};
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const reconciled = reconcileMergedAnalysis(model, local, { customerCount: 2, messageCount: 8 });
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assert.equal(reconciled.results[0].occurrence_count, 4);
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assert.equal(reconciled.results[0].affected_customer_count, 2);
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assert.equal(reconciled.results[0].buyer_intent, '优化后的诉求');
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assert.deepEqual(reconciled.results[0].affected_conversation_ids, ['u1']);
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assert.equal(reconciled.summary.conversation_count, 2);
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assert.equal(reconciled.summary.pre_sale_conversation_count, 2);
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});
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test('analysis normalization keeps every valid affected conversation id', () => {
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const normalized = normalizeAnalysis({
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summary: {},
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results: [{
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consultation_type: '售前',
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primary_reason: '产品问题',
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secondary_reason: '尺寸问题',
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occurrence_count: 3,
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affected_customer_count: 3,
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affected_conversation_ids: ['u1', 'u2', 'not-in-batch'],
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evidence: [{ conversation_id: 'u3', text: '尺寸是多少' }],
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}],
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}, { customerCount: 3, messageCount: 10 }, {
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allowedConversationIds: ['u1', 'u2', 'u3'],
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});
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assert.deepEqual(normalized.results[0].affected_conversation_ids, ['u1', 'u2', 'u3']);
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assert.equal(normalized.results[0].affected_customer_count, 3);
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});
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test('Qwen JSON parsing, usage aggregation and error extraction are provider-safe', () => {
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assert.deepEqual(parseModelJson('```json\n{"ok":true}\n```'), { ok: true });
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assert.deepEqual(sumUsages([
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{ prompt_tokens: 10, completion_tokens: 3, total_tokens: 13 },
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{ input_tokens: 7, output_tokens: 2 },
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]), { prompt_tokens: 17, completion_tokens: 5, total_tokens: 22 });
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assert.equal(getAiErrorDetail({ response: { data: { error: { message: 'rate limited' } } } }), 'rate limited');
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assert.equal(getAiErrorDetail({ message: 'Bearer sk-secret-example-1234567890 failed' }), 'Bearer [redacted] failed');
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assert.equal(isRetryableAiError({ code: 'ETIMEDOUT' }), true);
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assert.equal(isRetryableAiError({ response: { status: 429 } }), true);
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assert.equal(isRetryableAiError({ response: { status: 401 } }), false);
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assert.equal(getAiRetryDelayMs({}, 1, 1500), 1500);
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assert.equal(getAiRetryDelayMs({}, 2, 1500), 3000);
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assert.equal(getAiRetryDelayMs({ response: { headers: { 'retry-after': '2' } } }, 1, 1500), 2000);
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assert.equal(sanitizePublicError('千问模型请求失败：qwen-timeout'), 'AI服务请求失败：AI服务');
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assert.equal(sanitizePublicError('https://apihub.agnes-ai.com/v1 failed'), 'https://AI服务/v1 failed');
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});
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