Files
AIclinicalresearch/backend/src/tests/test-cross-language-search.ts
HaHafeng 2481b786d8 deploy: Complete 0126-27 deployment - database upgrade, services update, code recovery
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
2026-01-27 08:13:27 +08:00

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/**
* 跨语言检索测试
*
* 对比:
* 1. 纯 v4 跨语言1024维
* 2. v4 跨语言2048维
* 3. v4 + DeepSeek V3 查询重写
*
* 运行: npx tsx src/tests/test-cross-language-search.ts
*/
import { config } from 'dotenv';
config();
import { PrismaClient } from '@prisma/client';
import { getVectorSearchService } from '../common/rag/index';
const prisma = new PrismaClient();
// 中文查询测试集
const TEST_QUERIES = [
'这篇文档的主要研究内容是什么',
'银杏叶对老年痴呆有什么效果',
'临床试验的主要结论',
'研究方法和设计',
'研究对象的纳入标准',
];
async function testCrossLanguageSearch() {
console.log('========================================');
console.log('🌍 跨语言检索对比测试');
console.log('========================================\n');
// 查找 Dongen 2003.pdf 的文档
const document = await prisma.ekbDocument.findFirst({
where: { filename: 'Dongen 2003.pdf' },
select: { id: true, kbId: true, filename: true },
});
if (!document) {
console.error('❌ 测试文档不存在');
console.log(' 请先运行: npx tsx src/tests/test-pdf-ingest.ts <pdf路径>');
process.exit(1);
}
console.log(`✅ 找到测试文档: ${document.filename}`);
console.log(` kbId: ${document.kbId}`);
console.log(` docId: ${document.id}`);
console.log('');
const searchService = getVectorSearchService(prisma);
// 当前配置
const currentDimensions = parseInt(process.env.TEXT_EMBEDDING_DIMENSIONS || '1024', 10);
console.log(`📊 当前向量维度: ${currentDimensions}`);
console.log('');
console.log('开始测试(降低阈值到 0.2');
console.log('='.repeat(60));
for (const query of TEST_QUERIES) {
console.log(`\n🔍 查询: "${query}"`);
console.log('-'.repeat(60));
try {
const results = await searchService.vectorSearch(query, {
topK: 3,
minScore: 0.2, // 跨语言场景降低阈值
filter: { kbId: document.kbId },
enableQueryRewrite: false, // 先不用查询重写,看纯 v4 效果
});
if (results.length === 0) {
console.log(' ❌ 无结果(相似度 < 0.2');
} else {
console.log(` ✅ 返回 ${results.length} 条结果:`);
results.forEach((r, i) => {
const preview = r.content.substring(0, 70).replace(/\n/g, ' ');
console.log(` ${i + 1}. [${r.score.toFixed(3)}] ${preview}...`);
});
}
} catch (error) {
console.log(` ❌ 检索失败: ${error}`);
}
}
console.log('\n');
console.log('========================================');
console.log('📝 测试结论');
console.log('========================================');
console.log('');
console.log(`当前配置: text-embedding-v4 (${currentDimensions}维)`);
console.log('');
console.log('优化建议:');
console.log(' 1. ✅ 如果大部分查询有结果且相似度 > 0.25');
console.log(' → v4 跨语言能力足够,保持当前配置');
console.log('');
console.log(' 2. ⚠️ 如果相似度低于 0.25 或无结果:');
console.log(' → 建议升级到 2048 维提升15-40%');
console.log(' → 或启用 DeepSeek V3 查询重写');
console.log('');
console.log(' 3. 🎯 最佳方案2048维 + 查询重写');
console.log(' → 成本增加 <¥0.001/次');
console.log(' → 精度提升 50%+');
await prisma.$disconnect();
}
testCrossLanguageSearch();