HaHafeng
88cc049fb3
- 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
108 lines
3.4 KiB
TypeScript
108 lines
3.4 KiB
TypeScript
/**
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* LLM模型验证脚本
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* 用于验证实际接入的是哪个版本的模型
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*/
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import { LLMFactory } from '../src/common/llm/adapters/LLMFactory.js';
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import { logger } from '../src/common/logging/index.js';
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const TEST_PROMPT = "请用一句话简单介绍你自己，包括你的模型名称和版本。";
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async function verifyModel(modelType: string, expectedModel: string) {
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console.log(`\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━`);
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console.log(`🔍 验证模型: ${modelType}`);
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console.log(`━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━`);
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try {
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const adapter = LLMFactory.getAdapter(modelType as any);
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console.log(`✅ 适配器创建成功`);
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console.log(` 模型名称: ${adapter.modelName}`);
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console.log(` 期望模型: ${expectedModel}`);
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console.log(` 匹配状态: ${adapter.modelName === expectedModel ? '✅ 正确' : '❌ 不匹配'}`);
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console.log(`\n🚀 发送测试请求...`);
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const startTime = Date.now();
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const response = await adapter.chat([
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{ role: 'user', content: TEST_PROMPT }
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]);
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const duration = Date.now() - startTime;
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console.log(`\n📊 响应结果:`);
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console.log(` 实际返回模型: ${response.model}`);
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console.log(` 响应时间: ${duration}ms`);
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console.log(` Token使用:`);
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console.log(` - 输入: ${response.usage?.promptTokens || 0}`);
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console.log(` - 输出: ${response.usage?.completionTokens || 0}`);
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console.log(` - 总计: ${response.usage?.totalTokens || 0}`);
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console.log(`\n💬 模型回复:`);
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console.log(` "${response.content}"`);
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// 验证是否匹配
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if (response.model === expectedModel) {
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console.log(`\n✅ 验证通过！实际调用的就是 ${expectedModel}`);
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return true;
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} else {
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console.log(`\n⚠️ 警告！期望 ${expectedModel}，实际返回 ${response.model}`);
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return false;
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}
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} catch (error) {
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console.error(`\n❌ 验证失败:`, error);
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return false;
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}
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}
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async function main() {
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console.log('\n🔬 ASL模块LLM模型验证工具');
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console.log('=' .repeat(60));
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console.log('用途: 验证实际接入的模型版本是否正确\n');
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const models = [
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{ type: 'deepseek-v3', expected: 'deepseek-chat', description: 'DeepSeek-V3' },
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{ type: 'qwen3-72b', expected: 'qwen-max', description: 'Qwen最新最强模型' },
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];
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const results: { model: string; passed: boolean }[] = [];
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for (const model of models) {
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const passed = await verifyModel(model.type, model.expected);
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results.push({ model: model.description, passed });
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// 避免API限流
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await new Promise(resolve => setTimeout(resolve, 2000));
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}
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// 总结
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console.log('\n\n' + '='.repeat(60));
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console.log('📊 验证总结');
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console.log('='.repeat(60));
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results.forEach(r => {
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console.log(`${r.passed ? '✅' : '❌'} ${r.model}: ${r.passed ? '通过' : '未通过'}`);
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});
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const allPassed = results.every(r => r.passed);
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if (allPassed) {
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console.log('\n🎉 所有模型验证通过！');
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} else {
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console.log('\n⚠️ 部分模型验证未通过，请检查配置！');
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}
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console.log('='.repeat(60) + '\n');
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}
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main().catch(console.error);
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