HarmonyOS5.0与HarmonyOS SDK AI：基于RAG+端云大模型的教育问答系统精准性突破 原创

graph LR
A[学生提问] --> B[本地语义检索]
B --> C{结果置信度>0.9?}
C -->|Yes| D[端侧小模型回答]
C -->|No| E[云端大模型+RAG]
E --> F[知识库向量检索]
F --> G[精确答案生成]
G --> H[跨设备同步]

import { RagEngine, CloudModel, DeviceModel } from '@ohos.ai.qa';

class EduQAEngine {
  private localModel: DeviceModel;
  private cloudModel: CloudModel;
  private ragEngine: RagEngine;
  private knowledgeBase: string = 'distributed://edu_knowledge';
  
  constructor() {
    // 加载本地模型（轻量级）
    this.localModel = new DeviceModel({
      model: 'qa_lite_v3.hmod',
      acceleration: 'NPU'
    });
    
    // 云端大模型连接
    this.cloudModel = new CloudModel({
      endpoint: 'api.edu.ai.huawei.com',
      apiKey: 'xxxx',
      modelSize: '200B'
    });
    
    // RAG引擎初始化
    this.ragEngine = new RagEngine({
      knowledgeBase: this.knowledgeBase,
      retrievalSize: 5,
      hybridSearch: true
    });
  }
  
  // 问答处理核心方法
  async answer(question: string): Promise<Answer> {
    // 本地模型初判
    const localAnswer = await this.localModel.predict(question);
    if (localAnswer.confidence > 0.9) {
      return localAnswer;
    }
    
    // RAG增强处理
    return this.processWithRAG(question);
  }
  
  // RAG增强问答流程
  private async processWithRAG(question: string): Promise<Answer> {
    // 检索相关文档
    const retrievedDocs = await this.ragEngine.retrieve(question);
    
    // 大模型生成答案
    const messages: ChatMessage[] = [
      { role: 'system', content: '你是一名教育专家，根据文档回答问题' },
      { role: 'context', content: retrievedDocs.join('\n\n') },
      { role: 'user', content: question }
    ];
    
    return this.cloudModel.chatCompletion(messages, {
      maxTokens: 500,
      temperature: 0.3
    });
  }
}

// 教育知识库构建与同步
import { vectorDB } from '@ohos.ai.vectordb';
import { distributedData } from '@ohos.data.distributedData';

class KnowledgeRepository {
  private vectorDB: vectorDB.VectorStore;
  
  constructor() {
    // 初始化向量数据库
    this.vectorDB = vectorDB.createStore({
      name: 'edu_knowledge',
      dimension: 1536, // 使用CLIP-ViT模型维度
      distanceMetric: 'cosine'
    });
  }
  
  // 添加教育资源
  async addResource(resource: EduResource) {
    // 生成向量表示
    const embedding = await this.generateEmbedding(resource.content);
    
    // 存储到本地向量库
    await this.vectorDB.add({
      id: resource.id,
      vector: embedding,
      metadata: {
        title: resource.title,
        subject: resource.subject,
        grade: resource.grade
      }
    });
    
    // 分布式同步
    distributedData.sync('knowledge_vector', embedding, {
      devices: ['cloud', 'tablet', 'pc']
    });
  }
  
  // 语义相似度搜索
  async semanticSearch(query: string, k: number = 5) {
    const queryEmbedding = await this.generateEmbedding(query);
    const results = await this.vectorDB.search(queryEmbedding, k);
    return results.map(r => r.metadata);
  }
  
  // 本地嵌入生成（使用设备端模型）
  private async generateEmbedding(text: string): Promise<number[]> {
    const embedder = new TextEmbedder({
      model: 'text-embedding-mini.hmod',
      quantized: true
    });
    return embedder.encode(text);
  }
}

import { CoordinatorClient } from '@ohos.cloud.ai';
import { DeviceAI } from '@ohos.ai.device';

class HybridAIProcessor {
  private coordinator = new CoordinatorClient();
  private deviceAI = new DeviceAI();
  
  // 混合推理模式
  async hybridInference(context: Context): Promise<any> {
    // 设备端AI处理核心逻辑
    const deviceResult = await this.deviceAI.process(context);
    
    // 需要增强处理的情况
    if (deviceResult.needsEnhancement) {
      const cloudTaskId = this.coordinator.createTask({
        context,
        requirements: {
          modelSize: 'large',
          ragEnabled: true
        }
      });
      
      // 状态监听器
      const listener = this.coordinator.watchTask(cloudTaskId, {
        onProgress: (p) => Logger.info(`云端处理进度: ${p}%`),
        onResult: (res) => this.handleCloudResult(res),
        onError: (err) => this.handleCloudError(err)
      });
      
      // 启动协同任务
      listener.start();
      return { status: 'pending', taskId: cloudTaskId };
    }
    
    return deviceResult;
  }
  
  // 云端结果处理
  private handleCloudResult(result: any) {
    // 缓存结果到本地
    DistributedCache.set(result.key, result.data);
    
    // 更新设备端模型
    if (result.modelUpdate) {
      ModelUpdater.applyDelta(result.modelUpdate);
    }
  }
}

class RetrievalOptimizer {
  // 混合检索策略
  async hybridRetrieval(query: string): Promise<Document[]> {
    // 并行关键词和语义检索
    const [keywordResults, semanticResults] = await Promise.all([
      this.keywordSearch(query),
      this.semanticSearch(query)
    ]);
    
    // 多阶段融合
    return this.rrfFusion([keywordResults, semanticResults]);
  }
  
  // 多模型重新排序
  async rerankDocuments(query: string, docs: Document[]) {
    const reranker = new CrossEncoder({
      model: 'ranker-qa-v2.hmod'
    });
    return reranker.rerank(query, docs);
  }
  
  // 互惠排序融合（RRF）
  private rrfFusion(results: Document[][]): Document[] {
    const fusedScores = new Map<string, number>();
    const k = 60; // 融合常数
    
    results.forEach(list => {
      list.forEach((doc, rank) => {
        const score = fusedScores.get(doc.id) || 0;
        fusedScores.set(doc.id, score + 1 / (k + rank));
      });
    });
    
    return Array.from(fusedScores)
      .sort((a, b) => b[1] - a[1])
      .map(item => this.getDocById(item[0]));
  }
}

import { KnowledgeGraph } from '@ohos.knowledge.graph';

class KGAugmentedAnswer {
  private kg = KnowledgeGraph.load('academic_kg_v3');
  
  async augmentAnswer(answer: Answer, question: string) {
    // 提取答案中的实体
    const entities = this.extractEntities(answer.text);
    
    // 构建图谱上下文
    const subgraph = await this.kg.getSubgraph(entities, 2);
    
    // 生成增强型答案
    const augmented = await this.cloudModel.generate({
      template: 'answer_augmentation',
      context: {
        base_answer: answer.text,
        knowledge_graph: subgraph,
        question
      }
    });
    
    // 添加可视化支持
    return VisualizationHelper.addGraphPreview(augmented, subgraph);
  }
  
  private extractEntities(text: string): string[] {
    const ner = new EntityRecognizer();
    return ner.extract(text).map(e => e.text);
  }
}

// 模型量化工具
ModelQuantizer.quantize({
  model: 'qa_model_full.hmod',
  output: 'qa_model_quant.hmod',
  config: {
    quantization: 'int8',
    hybridQuant: true,
    skipLayers: ['output_layer'],
  }
});

// 部署到NPU
NPUCompiler.compile({
  model: 'qa_model_quant.hmod',
  target: 'kirin990',
  optimization: {
    fuseOps: true,
    precision: 'FP16'
  }
});

class TeachingAssistant {
  private qaEngine = new EduQAEngine();
  
  // 课堂实时问答
  @StatefulTask('live_qa')
  async handleLiveQuestion(question: string) {
    const startTime = Date.now();
    const answer = await this.qaEngine.answer(question);
    const processTime = Date.now() - startTime;
    
    // 生成教学补充
    const supplements = await this.generateSupplements(answer);
    
    return {
      answer: answer.text,
      supplements,
      confidence: answer.confidence,
      sources: answer.sources,
      latency: processTime
    };
  }
  
  // 错误检测与修正
  async detectAndCorrect(userAnswer: string, questionId: string) {
    // 检索标准答案
    const goldAnswer = await KnowledgeRepo.getAnswer(questionId);
    
    // 错误识别
    const errorReport = await ErrorDetector.compareAnswers(
      userAnswer, 
      goldAnswer
    );
    
    // 生成错题本
    if (errorReport.score < 0.7) {
      await this.addToErrorBook(questionId, userAnswer);
    }
    
    // 返回定制化解释
    return CorrectionGenerator.generate({
      userAnswer,
      goldAnswer,
      errors: errorReport.details,
      style: 'constructive'
    });
  }
}

class DistributedLearningSession {
  private syncManager = new DistributedDataSync();
  
  async startSession(primaryDevice: Device) {
    // 创建学习会话
    const session = await LearningSession.create();
    
    // 添加协同设备
    await session.addDevices([
      DeviceManager.getTablet(),
      DeviceManager.getSmartWatch(),
      DeviceManager.getTV()
    ]);
    
    // 分发学习任务
    const tasks = {
      tablet: 'qa_module',
      watch: 'reminder',
      tv: 'visualization'
    };
    
    this.syncManager.setData('session_tasks', tasks);
    return session;
  }
  
  // 会话状态共享
  async updateSessionState(state: LearningState) {
    // 压缩状态数据
    const compressed = CompressionUtil.zstdCompress(state, { level: 19 });
    
    // 增量同步
    return this.syncManager.patchData('session_state', compressed, {
      priority: 'HIGH',
      concurrent: true
    });
  }
  
  // AI驱动状态恢复
  async recoverSession() {
    const reconstructor = new SessionReconstructor();
    return reconstructor.recover({
      lastEvents: EventLog.getLast(10),
      devices: this.getSessionDevices()
    });
  }
}

// 跨场景教学支持
MetaClassroom.createSession({
  subject: 'physics',
  level: 'high-school',
  students: [student1, student2],
  resources: ['quantum_mechanics', 'relativity']
}).then(session => {
  // AI助教分配
  session.assignAssistant(new AITeachingAssistant('quantum_specialist'));
  
  // 动态知识映射
  session.connectKnowledgeGraph('physics_knowledge_map', {
    depth: 4,
    relations: ['prerequisite', 'real_world_app']
  });
  
  // RAG增强备课
  session.prepareLesson({
    augmentationLevel: 'deep',
    studentAdaptation: true
  });
});

// 量子增强AI原型
const quantumQA = QuantumEnhancedQA.create({
  baseEngine: eduQAEngine,
  quantumProcessor: 'kunpeng_quantum_accelerator',
  augmentationMode: 'hybrid_quantum_classical'
});

// 教育元宇宙集成
MetaverseCampus.join('physics_classroom_2030', {
  avatar: studentAvatar,
  permissions: ['interact', 'experiment'],
  aiCompanion: 'physics_tutor_ai'
});