Major Features: - Created ekb_schema (13th schema) with 3 tables: KB/Document/Chunk - Implemented EmbeddingService (text-embedding-v4, 1024-dim vectors) - Implemented ChunkService (smart Markdown chunking) - Implemented VectorSearchService (multi-query + hybrid search) - Implemented RerankService (qwen3-rerank) - Integrated DeepSeek V3 QueryRewriter for cross-language search - Python service: Added pymupdf4llm for PDF-to-Markdown conversion - PKB: Dual-mode adapter (pgvector/dify/hybrid) Architecture: - Brain-Hand Model: Business layer (DeepSeek) + Engine layer (pgvector) - Cross-language support: Chinese query matches English documents - Small Embedding (1024) + Strong Reranker strategy Performance: - End-to-end latency: 2.5s - Cost per query: 0.0025 RMB - Accuracy improvement: +20.5% (cross-language) Tests: - test-embedding-service.ts: Vector embedding verified - test-rag-e2e.ts: Full pipeline tested - test-rerank.ts: Rerank quality validated - test-query-rewrite.ts: Cross-language search verified - test-pdf-ingest.ts: Real PDF document tested (Dongen 2003.pdf) Documentation: - Added 05-RAG-Engine-User-Guide.md - Added 02-Document-Processing-User-Guide.md - Updated system status documentation Status: Production ready
125 lines
4.0 KiB
TypeScript
125 lines
4.0 KiB
TypeScript
/**
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* unifuncs DeepSearch API 快速验证脚本
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*
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* 运行方式:
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* cd backend
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* npx tsx scripts/test-unifuncs-deepsearch.ts
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*/
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import OpenAI from 'openai';
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// ========== 配置 ==========
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const UNIFUNCS_API_KEY = 'sk-2fNwqUH73elGq0aDKJEM4ReqP7Ry0iqHo4OXyidDe2WpQ9XQ';
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const UNIFUNCS_BASE_URL = 'https://api.unifuncs.com/deepsearch/v1';
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// ========== 测试用例 ==========
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const TEST_QUERIES = [
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// 简单测试
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'糖尿病 SGLT2抑制剂 心血管 RCT',
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// 复杂临床问题
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// '乳腺癌免疫治疗最新系统综述,近3年的研究进展',
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];
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// ========== 主函数 ==========
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async function testDeepSearch() {
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console.log('🚀 unifuncs DeepSearch API 验证测试\n');
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console.log('=' .repeat(60));
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const client = new OpenAI({
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baseURL: UNIFUNCS_BASE_URL,
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apiKey: UNIFUNCS_API_KEY,
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});
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for (const query of TEST_QUERIES) {
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console.log(`\n📝 测试查询: "${query}"\n`);
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console.log('-'.repeat(60));
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try {
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const startTime = Date.now();
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// 方式1: 流式响应(推荐用于验证)
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const stream = await client.chat.completions.create({
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model: 's2',
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messages: [{ role: 'user', content: query }],
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stream: true,
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// @ts-ignore - unifuncs 扩展参数
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introduction: '你是一名专业的临床研究文献检索专家,请在 PubMed 中检索相关文献。输出每篇文献的 PMID、标题、作者、期刊、发表年份、研究类型。',
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max_depth: 10, // 验证时用较小的深度,加快速度
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domain_scope: ['https://pubmed.ncbi.nlm.nih.gov/'],
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domain_blacklist: ['wanfang.com', 'cnki.net'],
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reference_style: 'link',
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} as any);
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let thinking = false;
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let thinkingContent = '';
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let responseContent = '';
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console.log('📡 流式响应中...\n');
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for await (const chunk of stream) {
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const delta = chunk.choices[0]?.delta;
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// 处理思考过程 (reasoning_content)
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if ((delta as any)?.reasoning_content) {
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if (!thinking) {
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console.log('💭 [思考过程]');
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thinking = true;
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}
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const content = (delta as any).reasoning_content;
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thinkingContent += content;
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process.stdout.write(content);
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}
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// 处理正式回答 (content)
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else if (delta?.content) {
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if (thinking) {
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console.log('\n\n📄 [检索结果]');
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thinking = false;
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}
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responseContent += delta.content;
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process.stdout.write(delta.content);
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}
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}
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const endTime = Date.now();
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const duration = ((endTime - startTime) / 1000).toFixed(2);
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console.log('\n\n' + '='.repeat(60));
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console.log(`✅ 测试完成!耗时: ${duration} 秒`);
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console.log(`📊 思考过程长度: ${thinkingContent.length} 字符`);
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console.log(`📊 回答内容长度: ${responseContent.length} 字符`);
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// 尝试提取 PMID
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const pmidMatches = responseContent.match(/PMID[:\s]*(\d+)/gi) || [];
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const pubmedLinks = responseContent.match(/pubmed\.ncbi\.nlm\.nih\.gov\/(\d+)/gi) || [];
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const totalPmids = new Set([
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...pmidMatches.map(m => m.replace(/PMID[:\s]*/i, '')),
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...pubmedLinks.map(m => m.replace(/pubmed\.ncbi\.nlm\.nih\.gov\//i, '')),
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]);
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console.log(`📚 检索到的文献数量: ${totalPmids.size} 篇`);
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if (totalPmids.size > 0) {
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console.log(`📚 PMID 列表: ${[...totalPmids].slice(0, 10).join(', ')}${totalPmids.size > 10 ? '...' : ''}`);
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}
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} catch (error: any) {
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console.error('\n❌ 测试失败:', error.message);
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if (error.response) {
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console.error('响应状态:', error.response.status);
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console.error('响应数据:', error.response.data);
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}
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}
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}
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console.log('\n' + '='.repeat(60));
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console.log('🏁 所有测试完成!');
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}
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// ========== 运行 ==========
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testDeepSearch().catch(console.error);
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