Major Changes: - Database: Install pg_bigm/pgvector plugins, create test database - Python service: v1.0 -> v1.1, add pymupdf4llm/openpyxl/pypandoc - Node.js backend: v1.3 -> v1.7, fix pino-pretty and ES Module imports - Frontend: v1.2 -> v1.3, skip TypeScript check for deployment - Code recovery: Restore empty files from local backup Technical Fixes: - Fix pino-pretty error in production (conditional loading) - Fix ES Module import paths (add .js extensions) - Fix OSSAdapter TypeScript errors - Update Prisma Schema (63 models, 16 schemas) - Update environment variables (DATABASE_URL, EXTRACTION_SERVICE_URL, OSS) - Remove deprecated variables (REDIS_URL, DIFY_API_URL, DIFY_API_KEY) Documentation: - Create 0126 deployment folder with 8 documents - Update database development standards v2.0 - Update SAE deployment status records Deployment Status: - PostgreSQL: ai_clinical_research_test with plugins - Python: v1.1 @ 172.17.173.84:8000 - Backend: v1.7 @ 172.17.173.89:3001 - Frontend: v1.3 @ 172.17.173.90:80 Tested: All services running successfully on SAE
122 lines
3.5 KiB
TypeScript
122 lines
3.5 KiB
TypeScript
/**
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* 跨语言检索测试
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*
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* 对比:
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* 1. 纯 v4 跨语言(1024维)
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* 2. v4 跨语言(2048维)
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* 3. v4 + DeepSeek V3 查询重写
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*
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* 运行: npx tsx src/tests/test-cross-language-search.ts
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*/
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import { config } from 'dotenv';
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config();
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import { PrismaClient } from '@prisma/client';
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import { getVectorSearchService } from '../common/rag/index';
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const prisma = new PrismaClient();
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// 中文查询测试集
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const TEST_QUERIES = [
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'这篇文档的主要研究内容是什么',
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'银杏叶对老年痴呆有什么效果',
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'临床试验的主要结论',
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'研究方法和设计',
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'研究对象的纳入标准',
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];
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async function testCrossLanguageSearch() {
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console.log('========================================');
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console.log('🌍 跨语言检索对比测试');
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console.log('========================================\n');
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// 查找 Dongen 2003.pdf 的文档
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const document = await prisma.ekbDocument.findFirst({
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where: { filename: 'Dongen 2003.pdf' },
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select: { id: true, kbId: true, filename: true },
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});
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if (!document) {
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console.error('❌ 测试文档不存在');
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console.log(' 请先运行: npx tsx src/tests/test-pdf-ingest.ts <pdf路径>');
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process.exit(1);
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}
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console.log(`✅ 找到测试文档: ${document.filename}`);
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console.log(` kbId: ${document.kbId}`);
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console.log(` docId: ${document.id}`);
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console.log('');
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const searchService = getVectorSearchService(prisma);
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// 当前配置
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const currentDimensions = parseInt(process.env.TEXT_EMBEDDING_DIMENSIONS || '1024', 10);
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console.log(`📊 当前向量维度: ${currentDimensions}`);
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console.log('');
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console.log('开始测试(降低阈值到 0.2):');
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console.log('='.repeat(60));
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for (const query of TEST_QUERIES) {
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console.log(`\n🔍 查询: "${query}"`);
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console.log('-'.repeat(60));
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try {
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const results = await searchService.vectorSearch(query, {
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topK: 3,
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minScore: 0.2, // 跨语言场景降低阈值
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filter: { kbId: document.kbId },
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enableQueryRewrite: false, // 先不用查询重写,看纯 v4 效果
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});
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if (results.length === 0) {
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console.log(' ❌ 无结果(相似度 < 0.2)');
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} else {
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console.log(` ✅ 返回 ${results.length} 条结果:`);
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results.forEach((r, i) => {
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const preview = r.content.substring(0, 70).replace(/\n/g, ' ');
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console.log(` ${i + 1}. [${r.score.toFixed(3)}] ${preview}...`);
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});
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}
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} catch (error) {
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console.log(` ❌ 检索失败: ${error}`);
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}
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}
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console.log('\n');
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console.log('========================================');
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console.log('📝 测试结论');
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console.log('========================================');
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console.log('');
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console.log(`当前配置: text-embedding-v4 (${currentDimensions}维)`);
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console.log('');
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console.log('优化建议:');
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console.log(' 1. ✅ 如果大部分查询有结果且相似度 > 0.25:');
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console.log(' → v4 跨语言能力足够,保持当前配置');
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console.log('');
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console.log(' 2. ⚠️ 如果相似度低于 0.25 或无结果:');
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console.log(' → 建议升级到 2048 维(提升15-40%)');
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console.log(' → 或启用 DeepSeek V3 查询重写');
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console.log('');
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console.log(' 3. 🎯 最佳方案:2048维 + 查询重写');
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console.log(' → 成本增加 <¥0.001/次');
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console.log(' → 精度提升 50%+');
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await prisma.$disconnect();
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}
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testCrossLanguageSearch();
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