feat(asl): Complete Day 5 - Fulltext Screening Backend API Development
- Implement 5 core API endpoints (create task, get progress, get results, update decision, export Excel) - Add FulltextScreeningController with Zod validation (652 lines) - Implement ExcelExporter service with 4-sheet report generation (352 lines) - Register routes under /api/v1/asl/fulltext-screening - Create 31 REST Client test cases - Add automated integration test script - Fix PDF extraction fallback mechanism in LLM12FieldsService - Update API design documentation to v3.0 - Update development plan to v1.2 - Create Day 5 development record - Clean up temporary test files
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/**
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* 端到端真实测试 v2 - 简化版
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*
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* 使用真实数据测试完整流程：
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* 1. 创建项目
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* 2. 导入1篇文献（简化）
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* 3. 创建全文复筛任务
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* 4. 等待LLM处理
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* 5. 查看结果
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*/
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import axios from 'axios';
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import { PrismaClient } from '@prisma/client';
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import fs from 'fs/promises';
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import path from 'path';
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const API_BASE = 'http://localhost:3000/api/v1/asl';
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const prisma = new PrismaClient();
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interface TestResult {
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projectId?: string;
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literatureIds?: string[];
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taskId?: string;
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success: boolean;
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error?: string;
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}
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async function runTest(): Promise<TestResult> {
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console.log('🚀 开始端到端真实测试 v2\n');
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console.log('⏰ 测试时间:', new Date().toLocaleString('zh-CN'));
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console.log('📍 API地址:', API_BASE);
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console.log('=' .repeat(80) + '\n');
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const result: TestResult = { success: false };
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try {
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// ========================================
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// Step 1: 创建测试项目
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// ========================================
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console.log('📋 Step 1: 创建测试项目');
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const picosPath = path.join(
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process.cwd(),
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'../docs/03-业务模块/ASL-AI智能文献/05-测试文档/03-测试数据/screening/测试案例的PICOS、纳入标准、排除标准.txt'
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);
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const picosContent = await fs.readFile(picosPath, 'utf-8');
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// 解析PICOS
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const populationMatch = picosContent.match(/P \(Population\)[：:]\s*(.+)/);
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const interventionMatch = picosContent.match(/I \(Intervention\)[：:]\s*(.+)/);
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const comparisonMatch = picosContent.match(/C \(Comparison\)[：:]\s*(.+)/);
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const outcomeMatch = picosContent.match(/O \(Outcome\)[：:]\s*(.+)/);
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const studyDesignMatch = picosContent.match(/S \(Study Design\)[：:]\s*(.+)/);
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const projectData = {
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name: `E2E测试-${Date.now()}`,
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description: '端到端真实测试项目',
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picoCriteria: {
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P: populationMatch?.[1]?.trim() || '缺血性卒中患者',
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I: interventionMatch?.[1]?.trim() || '抗血小板治疗',
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C: comparisonMatch?.[1]?.trim() || '对照组',
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O: outcomeMatch?.[1]?.trim() || '卒中复发',
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S: studyDesignMatch?.[1]?.trim() || 'RCT',
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},
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};
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const projectResponse = await axios.post(`${API_BASE}/projects`, projectData);
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result.projectId = projectResponse.data.data.id;
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console.log(`✅ 项目创建成功: ${result.projectId}\n`);
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// ========================================
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// Step 2: 导入1篇简单测试文献
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// ========================================
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console.log('📚 Step 2: 导入测试文献（使用简化数据）');
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const literatureData = {
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projectId: result.projectId,
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literatures: [
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{
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pmid: 'TEST001',
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title: 'Antiplatelet Therapy for Secondary Stroke Prevention: A Randomized Controlled Trial',
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abstract: 'Background: Stroke is a major cause of death worldwide. This study evaluates antiplatelet therapy effectiveness. Methods: We conducted an RCT with 500 patients randomized to aspirin vs clopidogrel groups. The study was double-blind. Results: Primary outcome (stroke recurrence) occurred in 12% of aspirin group vs 8% of clopidogrel group (p=0.03). Secondary outcomes showed similar trends. Conclusion: Clopidogrel demonstrates superior efficacy for secondary stroke prevention in Asian patients.',
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authors: 'Zhang W, Li H, Wang Y',
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journal: 'Stroke Research',
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publicationYear: 2023,
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hasPdf: false,
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},
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],
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};
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const importResponse = await axios.post(`${API_BASE}/literatures/import`, literatureData);
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console.log(`✅ 文献导入成功: ${importResponse.data.data.importedCount}篇\n`);
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// 获取文献ID
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const literatures = await prisma.aslLiterature.findMany({
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where: { projectId: result.projectId },
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select: { id: true, title: true },
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});
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result.literatureIds = literatures.map(lit => lit.id);
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console.log('📄 导入的文献:');
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literatures.forEach(lit => {
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console.log(` - ${lit.id.slice(0, 8)}: ${lit.title.slice(0, 60)}...`);
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});
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console.log('');
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// ========================================
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// Step 3: 创建全文复筛任务
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// ========================================
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console.log('🤖 Step 3: 创建全文复筛任务');
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const taskData = {
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projectId: result.projectId,
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literatureIds: result.literatureIds,
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config: {
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modelA: 'deepseek-v3',
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modelB: 'qwen-max',
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concurrency: 1,
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skipExtraction: true, // 跳过PDF提取，使用标题+摘要
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},
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};
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const taskResponse = await axios.post(`${API_BASE}/fulltext-screening/tasks`, taskData);
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result.taskId = taskResponse.data.data.taskId;
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console.log(`✅ 任务创建成功: ${result.taskId}\n`);
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// ========================================
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// Step 4: 监控任务进度
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// ========================================
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console.log('⏳ Step 4: 监控任务进度（等待LLM处理）\n');
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let maxAttempts = 30; // 最多等待5分钟
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let attempt = 0;
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let taskCompleted = false;
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while (attempt < maxAttempts && !taskCompleted) {
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await new Promise(resolve => setTimeout(resolve, 10000)); // 每10秒查询一次
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attempt++;
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try {
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const progressResponse = await axios.get(
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`${API_BASE}/fulltext-screening/tasks/${result.taskId}/progress`
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);
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const progress = progressResponse.data.data;
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console.log(`[${attempt}/${maxAttempts}] 进度: ${progress.processedCount}/${progress.totalCount} | ` +
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`成功: ${progress.successCount} | 失败: ${progress.failedCount} | ` +
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`Token: ${progress.totalTokens} | 成本: ¥${progress.totalCost.toFixed(4)}`);
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if (progress.status === 'completed' || progress.status === 'failed') {
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taskCompleted = true;
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console.log(`\n✅ 任务完成！状态: ${progress.status}\n`);
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}
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} catch (error: any) {
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console.log(`⚠️ 查询进度失败: ${error.message}`);
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}
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}
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if (!taskCompleted) {
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console.log('⚠️ 任务超时，但可能仍在后台处理\n');
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}
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// ========================================
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// Step 5: 获取结果
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// ========================================
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console.log('📊 Step 5: 获取处理结果\n');
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try {
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const resultsResponse = await axios.get(
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`${API_BASE}/fulltext-screening/tasks/${result.taskId}/results`
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);
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const results = resultsResponse.data.data;
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console.log('=' .repeat(80));
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console.log('📈 最终统计:');
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console.log(` - 总文献数: ${results.results.length}`);
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console.log(` - 总Token: ${results.summary.totalTokens}`);
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console.log(` - 总成本: ¥${results.summary.totalCost.toFixed(4)}`);
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console.log('');
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if (results.results.length > 0) {
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console.log('📄 文献结果详情:');
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results.results.forEach((r: any, idx: number) => {
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console.log(`\n[${idx + 1}] ${r.literatureTitle}`);
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console.log(` Model A (${r.modelAName}): ${r.modelAStatus}`);
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console.log(` Model B (${r.modelBName}): ${r.modelBStatus}`);
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console.log(` Token: ${r.modelATokens + r.modelBTokens}`);
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console.log(` 成本: ¥${(r.modelACost + r.modelBCost).toFixed(4)}`);
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if (r.modelAStatus === 'success' && r.modelAOverall) {
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console.log(` 决策: ${r.modelAOverall.overall_decision || 'N/A'}`);
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}
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});
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}
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result.success = results.results.length > 0;
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} catch (error: any) {
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console.log(`❌ 获取结果失败: ${error.message}`);
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}
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console.log('\n' + '=' .repeat(80));
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console.log('🎉 测试完成！\n');
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} catch (error: any) {
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console.error('\n❌ 测试失败:', error.message);
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if (error.response?.data) {
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console.error('错误详情:', JSON.stringify(error.response.data, null, 2));
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}
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result.success = false;
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result.error = error.message;
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} finally {
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await prisma.$disconnect();
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}
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return result;
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}
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// 运行测试
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runTest()
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.then(result => {
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if (result.success) {
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console.log('✅ 端到端测试成功！');
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process.exit(0);
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} else {
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console.log('❌ 端到端测试失败');
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process.exit(1);
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}
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})
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.catch(error => {
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console.error('💥 测试执行异常:', error);
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process.exit(1);
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});
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