MCP Advisor

/** * 向量搜索引擎通用测试 * 测试各种向量搜索引擎实现的共同接口和行为 */ import { describe, test, expect, vi, beforeEach, afterEach } from 'vitest'; import { IVectorSearchEngine } from '../../../services/interfaces/vectorSearchEngine.js'; import { normalizeVector } from '../../../utils/vectorUtils.js'; import { MCPServerResponse } from '../../../types/index.js'; // 创建一个模拟的向量搜索引擎实现 class MockVectorSearchEngine implements IVectorSearchEngine { private mockData: Array<{ id: string; vector: number[]; data: MCPServerResponse }> = []; async addEntry(id: string, vector: number[], data: MCPServerResponse): Promise<void> { this.mockData.push({ id, vector, data }); } async search(queryVector: number[], limit: number = 10): Promise<MCPServerResponse[]> { // 模拟搜索实现 const results = this.mockData.map(item => { const similarity = this.calculateSimilarity(queryVector, item.vector); return { ...item.data, similarity // 更新相似度字段 }; }) .sort((a, b) => b.similarity - a.similarity) .slice(0, limit); return results; } async clear(): Promise<void> { this.mockData = []; } // 辅助方法：计算余弦相似度 private calculateSimilarity(v1: number[], v2: number[]): number { const normalizedV1 = normalizeVector(v1); const normalizedV2 = normalizeVector(v2); let dotProduct = 0; for (let i = 0; i < normalizedV1.length; i++) { dotProduct += normalizedV1[i] * normalizedV2[i]; } return dotProduct; } // 用于测试的方法：添加模拟数据 addMockData(id: string, vector: number[], metadata: Record<string, any>): void { // 创建一个简单的 MCPServerResponse 对象 const data: MCPServerResponse = { id, title: metadata.name || `MCP Server ${id}`, description: metadata.description || `Description for ${id}`, sourceUrl: metadata.github_url || `https://github.com/example/${id}`, similarity: 0, // 初始相似度为 0，在搜索时计算 categories: metadata.categories || 'test', tags: metadata.tags || ['test', 'mock'] }; this.mockData.push({ id, vector, data }); } // 用于测试的方法：清除模拟数据 clearMockData(): void { this.mockData = []; } } describe('向量搜索引擎通用测试', () => { let vectorEngine: MockVectorSearchEngine; beforeEach(() => { vectorEngine = new MockVectorSearchEngine(); // 添加一些测试数据 vectorEngine.addMockData('1', [1, 0, 0], { name: '文档1' }); vectorEngine.addMockData('2', [0, 1, 0], { name: '文档2' }); vectorEngine.addMockData('3', [0, 0, 1], { name: '文档3' }); vectorEngine.addMockData('4', [0.7, 0.7, 0], { name: '文档4' }); }); afterEach(() => { vectorEngine.clearMockData(); }); test('应该能够找到最相似的向量', async () => { const queryVector = [0.9, 0.1, 0]; const results = await vectorEngine.search(queryVector, 2); expect(results.length).toBe(2); expect(results[0].id).toBe('1'); // 文档1应该是最相似的 expect(results[0].similarity).toBeGreaterThan(0.9); }); test('应该返回按相似度排序的结果', async () => { const queryVector = [0.5, 0.5, 0.5]; const results = await vectorEngine.search(queryVector); // 确保结果按相似度降序排列 for (let i = 1; i < results.length; i++) { expect(results[i-1].similarity).toBeGreaterThanOrEqual(results[i].similarity); } }); test('应该限制返回结果数量', async () => { const queryVector = [0.33, 0.33, 0.33]; const limit = 2; const results = await vectorEngine.search(queryVector, limit); expect(results.length).toBeLessThanOrEqual(limit); }); // TODO: 添加更多测试用例 test.todo('应该正确处理空向量'); test.todo('应该正确处理不同维度的向量'); test.todo('应该正确处理大量数据'); });