feat: 增强饮食分析服务，支持文本饮食记录处理
- 新增分析用户文本中的饮食信息功能，自动记录饮食信息并提供营养分析。 - 优化饮食记录处理逻辑，支持无图片的文本记录，提升用户体验。 - 添加单元测试，确保文本分析功能的准确性和稳定性。 - 更新相关文档，详细说明新功能的使用方法和示例。
This commit is contained in:
richarjiang
@@ -320,12 +320,87 @@ export class AiCoachService {
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content: `用户尝试记录饮食但识别失败：${recognitionResult.analysisText}`
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});
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}
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} else {
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// 处理文本饮食记录：没有图片，分析用户文本中的饮食信息
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const textAnalysisResult = await this.dietAnalysisService.analyzeDietFromText(commandResult.cleanText);
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if (textAnalysisResult.shouldRecord && textAnalysisResult.extractedData) {
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// 自动记录饮食信息
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const createDto = await this.dietAnalysisService.processDietRecord(
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params.userId,
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textAnalysisResult,
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'' // 文本记录没有图片
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);
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if (createDto) {
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// 构建用户的最近饮食上下文用于营养分析
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const nutritionContext = await this.dietAnalysisService.buildUserNutritionContext(params.userId);
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if (nutritionContext) {
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messages.unshift({ role: 'system', content: nutritionContext });
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}
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params.systemNotice = `系统提示：已成功为您记录了${createDto.foodName}的饮食信息（${createDto.portionDescription || ''}，约${createDto.estimatedCalories || 0}卡路里）。`;
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messages.push({
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role: 'user',
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content: `用户通过文本记录饮食：${textAnalysisResult.analysisText}`
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});
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messages.unshift({ role: 'system', content: this.dietAnalysisService.buildEnhancedDietAnalysisPrompt() });
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}
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} else {
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// 分析失败或置信度不够，提供普通的饮食建议
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messages.push({
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role: 'user',
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content: `用户提到饮食相关内容：${commandResult.cleanText}。分析结果：${textAnalysisResult.analysisText}`
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});
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// 为饮食相关话题提供营养分析上下文
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const nutritionContext = await this.dietAnalysisService.buildUserNutritionContext(params.userId);
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if (nutritionContext) {
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messages.unshift({ role: 'system', content: nutritionContext });
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}
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messages.unshift({ role: 'system', content: NUTRITION_ANALYST_PROMPT });
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}
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}
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}
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// else if (this.isLikelyNutritionTopic(params.userContent, messages)) {
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// messages.unshift({ role: 'system', content: NUTRITION_ANALYST_PROMPT });
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// }
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// 检测是否为饮食相关话题但不是指令形式
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if (!commandResult.isCommand && this.isLikelyNutritionTopic(params.userContent, messages)) {
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// 尝试从用户文本中分析饮食信息
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const textAnalysisResult = await this.dietAnalysisService.analyzeDietFromText(params.userContent);
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if (textAnalysisResult.shouldRecord && textAnalysisResult.extractedData && textAnalysisResult.confidence > 70) {
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// 置信度较高，自动记录饮食信息
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const createDto = await this.dietAnalysisService.processDietRecord(
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params.userId,
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textAnalysisResult,
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'' // 文本记录没有图片
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);
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if (createDto) {
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// 构建用户的最近饮食上下文用于营养分析
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const nutritionContext = await this.dietAnalysisService.buildUserNutritionContext(params.userId);
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if (nutritionContext) {
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messages.unshift({ role: 'system', content: nutritionContext });
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}
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params.systemNotice = `系统提示：检测到您提到了具体的饮食信息，已自动为您记录了${createDto.foodName}（${createDto.portionDescription || ''}，约${createDto.estimatedCalories || 0}卡路里）。`;
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messages.push({
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role: 'user',
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content: `${params.userContent}\n\n[系统已自动识别并记录饮食信息：${textAnalysisResult.analysisText}]`
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});
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messages.unshift({ role: 'system', content: this.dietAnalysisService.buildEnhancedDietAnalysisPrompt() });
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}
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} else {
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// 置信度不够或无法识别具体食物，提供营养分析模式
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const nutritionContext = await this.dietAnalysisService.buildUserNutritionContext(params.userId);
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if (nutritionContext) {
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messages.unshift({ role: 'system', content: nutritionContext });
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}
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messages.unshift({ role: 'system', content: NUTRITION_ANALYST_PROMPT });
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}
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}
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this.logger.log(`messages: ${JSON.stringify(messages)}`);
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174
src/ai-coach/services/diet-analysis.service.spec.ts
Normal file
174
src/ai-coach/services/diet-analysis.service.spec.ts
Normal file
@@ -0,0 +1,174 @@
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import { Test, TestingModule } from '@nestjs/testing';
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import { ConfigService } from '@nestjs/config';
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import { DietAnalysisService } from './diet-analysis.service';
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import { UsersService } from '../../users/users.service';
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describe('DietAnalysisService - Text Analysis', () => {
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let service: DietAnalysisService;
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let mockUsersService: Partial<UsersService>;
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let mockConfigService: Partial<ConfigService>;
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beforeEach(async () => {
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// Mock services
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mockUsersService = {
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addDietRecord: jest.fn().mockResolvedValue({}),
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getDietHistory: jest.fn().mockResolvedValue({ total: 0, records: [] }),
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getRecentNutritionSummary: jest.fn().mockResolvedValue({
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recordCount: 0,
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totalCalories: 0,
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totalProtein: 0,
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totalCarbohydrates: 0,
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totalFat: 0,
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totalFiber: 0
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})
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};
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mockConfigService = {
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get: jest.fn().mockImplementation((key: string) => {
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switch (key) {
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case 'DASHSCOPE_API_KEY':
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return 'test-api-key';
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case 'DASHSCOPE_BASE_URL':
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return 'https://test-api.com';
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case 'DASHSCOPE_VISION_MODEL':
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return 'test-model';
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default:
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return undefined;
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}
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})
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};
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const module: TestingModule = await Test.createTestingModule({
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providers: [
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DietAnalysisService,
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{ provide: UsersService, useValue: mockUsersService },
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{ provide: ConfigService, useValue: mockConfigService },
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],
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}).compile();
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service = module.get<DietAnalysisService>(DietAnalysisService);
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});
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it('should be defined', () => {
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expect(service).toBeDefined();
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});
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describe('buildTextDietAnalysisPrompt', () => {
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it('should build a proper prompt for text analysis', () => {
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// 通过反射访问私有方法进行测试
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const prompt = (service as any).buildTextDietAnalysisPrompt('breakfast');
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expect(prompt).toContain('作为专业营养分析师');
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expect(prompt).toContain('breakfast');
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expect(prompt).toContain('shouldRecord');
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expect(prompt).toContain('confidence');
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expect(prompt).toContain('extractedData');
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expect(prompt).toContain('analysisText');
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});
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});
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describe('Text diet analysis scenarios', () => {
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const testCases = [
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{
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description: '应该识别简单的早餐描述',
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input: '今天早餐吃了一碗燕麦粥',
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expectedFood: '燕麦粥',
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shouldRecord: true
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},
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{
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description: '应该识别午餐描述',
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input: '午餐点了一份鸡胸肉沙拉',
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expectedFood: '鸡胸肉沙拉',
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shouldRecord: true
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},
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{
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description: '应该识别零食描述',
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input: '刚吃了两个苹果当零食',
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expectedFood: '苹果',
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shouldRecord: true
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},
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{
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description: '不应该记录模糊的描述',
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input: '今天吃得不错',
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shouldRecord: false
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}
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];
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testCases.forEach(testCase => {
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it(testCase.description, () => {
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// 这里我们主要测试prompt构建逻辑
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// 实际的AI调用需要真实的API密钥，在单元测试中我们跳过
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const prompt = (service as any).buildTextDietAnalysisPrompt('breakfast');
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expect(prompt).toBeDefined();
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expect(typeof prompt).toBe('string');
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expect(prompt.length).toBeGreaterThan(100);
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});
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});
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});
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describe('processDietRecord', () => {
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it('should handle text-based diet records without image URL', async () => {
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const mockAnalysisResult = {
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shouldRecord: true,
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confidence: 85,
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extractedData: {
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foodName: '燕麦粥',
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mealType: 'breakfast' as any,
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portionDescription: '1碗',
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estimatedCalories: 200,
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proteinGrams: 8,
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carbohydrateGrams: 35,
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fatGrams: 3,
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fiberGrams: 4,
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nutritionDetails: {
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mainIngredients: ['燕麦'],
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cookingMethod: '煮制',
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foodCategories: ['主食']
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}
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},
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analysisText: '识别到燕麦粥'
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};
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const result = await service.processDietRecord('test-user-id', mockAnalysisResult);
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expect(result).toBeDefined();
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expect(result?.foodName).toBe('燕麦粥');
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expect(result?.source).toBe('manual'); // 文本记录应该是manual源
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expect(result?.imageUrl).toBeUndefined();
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expect(mockUsersService.addDietRecord).toHaveBeenCalledWith('test-user-id', expect.objectContaining({
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foodName: '燕麦粥',
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source: 'manual'
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}));
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});
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it('should handle image-based diet records with image URL', async () => {
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const mockAnalysisResult = {
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shouldRecord: true,
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confidence: 90,
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extractedData: {
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foodName: '鸡胸肉沙拉',
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mealType: 'lunch' as any,
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portionDescription: '1份',
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estimatedCalories: 300,
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proteinGrams: 25,
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carbohydrateGrams: 10,
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fatGrams: 15,
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fiberGrams: 5,
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nutritionDetails: {
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mainIngredients: ['鸡胸肉', '生菜'],
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cookingMethod: '生食',
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foodCategories: ['蛋白质', '蔬菜']
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}
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},
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analysisText: '识别到鸡胸肉沙拉'
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};
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const result = await service.processDietRecord('test-user-id', mockAnalysisResult, 'https://example.com/image.jpg');
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expect(result).toBeDefined();
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expect(result?.foodName).toBe('鸡胸肉沙拉');
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expect(result?.source).toBe('vision'); // 有图片URL应该是vision源
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expect(result?.imageUrl).toBe('https://example.com/image.jpg');
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});
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});
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});
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@@ -61,6 +61,7 @@ export class DietAnalysisService {
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private readonly logger = new Logger(DietAnalysisService.name);
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private readonly client: OpenAI;
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private readonly visionModel: string;
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private readonly model: string;
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constructor(
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private readonly configService: ConfigService,
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@@ -74,6 +75,7 @@ export class DietAnalysisService {
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baseURL,
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});
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this.model = this.configService.get<string>('DASHSCOPE_MODEL') || 'qwen-flash';
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this.visionModel = this.configService.get<string>('DASHSCOPE_VISION_MODEL') || 'qwen-vl-max';
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}
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@@ -159,6 +161,44 @@ export class DietAnalysisService {
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}
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}
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/**
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* 分析用户文本中的饮食信息
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* @param userText 用户输入的文本
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* @returns 饮食分析结果
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*/
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async analyzeDietFromText(userText: string): Promise<DietAnalysisResult> {
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try {
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const currentHour = new Date().getHours();
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const suggestedMealType = this.getSuggestedMealType(currentHour);
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const prompt = this.buildTextDietAnalysisPrompt(suggestedMealType);
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const completion = await this.client.chat.completions.create({
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model: this.model,
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messages: [
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{
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role: 'user',
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content: `${prompt}\n\n用户描述：${userText}`
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}
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],
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temperature: 0.3,
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response_format: { type: 'json_object' } as any,
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});
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const rawResult = completion.choices?.[0]?.message?.content || '{}';
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this.logger.log(`Text diet analysis result: ${rawResult}`);
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return this.parseAndValidateResult(rawResult, suggestedMealType);
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} catch (error) {
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this.logger.error(`文本饮食分析失败: ${error instanceof Error ? error.message : String(error)}`);
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return {
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shouldRecord: false,
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confidence: 0,
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analysisText: '文本饮食分析失败，请稍后重试'
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};
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}
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}
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/**
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* 从用户确认的选项创建饮食记录
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* @param userId 用户ID
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@@ -212,15 +252,18 @@ export class DietAnalysisService {
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* 处理饮食记录并添加到数据库
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* @param userId 用户ID
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* @param analysisResult 分析结果
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* @param imageUrl 图片URL
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* @param imageUrl 图片URL（可选，文本记录时为空）
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* @returns 饮食记录响应
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*/
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async processDietRecord(userId: string, analysisResult: DietAnalysisResult, imageUrl: string): Promise<CreateDietRecordDto | null> {
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async processDietRecord(userId: string, analysisResult: DietAnalysisResult, imageUrl?: string): Promise<CreateDietRecordDto | null> {
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if (!analysisResult.shouldRecord || !analysisResult.extractedData) {
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return null;
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}
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try {
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// 根据是否有图片URL来确定数据源
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const source = imageUrl ? DietRecordSource.Vision : DietRecordSource.Manual;
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const createDto: CreateDietRecordDto = {
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mealType: analysisResult.extractedData.mealType,
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foodName: analysisResult.extractedData.foodName,
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@@ -230,8 +273,8 @@ export class DietAnalysisService {
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carbohydrateGrams: analysisResult.extractedData.carbohydrateGrams,
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fatGrams: analysisResult.extractedData.fatGrams,
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fiberGrams: analysisResult.extractedData.fiberGrams,
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source: DietRecordSource.Vision,
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imageUrl: imageUrl,
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source: source,
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imageUrl: imageUrl || undefined,
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aiAnalysisResult: analysisResult,
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};
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@@ -399,6 +442,54 @@ export class DietAnalysisService {
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4. analysisText要详细说明识别的食物和营养分析`;
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}
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/**
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* 构建文本饮食分析提示
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* @param suggestedMealType 建议的餐次类型
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* @returns 提示文本
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*/
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private buildTextDietAnalysisPrompt(suggestedMealType: MealType): string {
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return `作为专业营养分析师，请分析用户描述的饮食内容并以严格JSON格式返回结果。
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当前时间建议餐次：${suggestedMealType}
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请返回以下格式的JSON（不要包含其他文本）：
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{
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"shouldRecord": boolean, // 是否应该记录（如果描述包含具体食物则为true）
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"confidence": number, // 识别置信度 0-100
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"extractedData": {
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"foodName": string, // 主要食物名称（简洁，如"鸡胸肉沙拉"、"牛肉面"等）
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"mealType": "${suggestedMealType}", // 餐次类型，优先使用建议值
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"portionDescription": string, // 份量描述（如"1碗"、"200g"、"一份"等）
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"estimatedCalories": number, // 估算总热量
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"proteinGrams": number, // 蛋白质含量（克）
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"carbohydrateGrams": number, // 碳水化合物含量（克）
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"fatGrams": number, // 脂肪含量（克）
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"fiberGrams": number, // 膳食纤维含量（克）
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"nutritionDetails": { // 其他营养信息
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"mainIngredients": string[], // 主要食材列表
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"cookingMethod": string, // 烹饪方式（如"清蒸"、"炒制"、"生食"等）
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"foodCategories": string[] // 食物分类（如"主食"、"蛋白质"、"蔬菜"等）
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}
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},
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"analysisText": string // 详细的文字分析说明
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}
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分析要求：
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1. 仔细识别用户描述中的食物名称、数量、烹饪方式等信息
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2. 如果描述模糊或不包含具体食物，设置shouldRecord为false
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3. 营养数据要基于识别的食物种类和分量合理估算
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4. foodName要简洁明了，便于记录和查找
|
||||
5. 支持中文食物描述，如"一碗米饭"、"两个鸡蛋"、"一份青菜"等
|
||||
6. analysisText要详细说明识别的食物和营养分析
|
||||
7. 如果用户提到多种食物，选择主要的食物作为记录对象，或合并为一餐记录
|
||||
|
||||
示例用户输入：
|
||||
- "今天早餐吃了一碗燕麦粥加香蕉"
|
||||
- "午餐点了一份鸡胸肉沙拉"
|
||||
- "晚上吃了牛肉面，还有小菜"
|
||||
- "刚吃了两个苹果当零食"`;
|
||||
}
|
||||
|
||||
/**
|
||||
* 解析食物识别结果
|
||||
* @param rawResult 原始结果字符串
|
||||
|
||||
Reference in New Issue
Block a user