Spring AI与RAG技术实战：构建企业级智能文档问答系统

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Spring AI与RAG技术实战：构建企业级智能文档问答系统

引言

随着人工智能技术的快速发展，企业对于智能化文档处理的需求日益增长。传统的文档管理系统往往只能提供简单的关键词搜索，而无法理解用户的自然语言查询意图。Spring AI结合RAG（检索增强生成）技术，为企业构建智能文档问答系统提供了全新的解决方案。

技术架构概述

核心组件

我们的智能文档问答系统基于以下技术栈构建：

Spring AI: 提供统一的AI应用开发接口
RAG架构: 检索增强生成技术框架
向量数据库: Milvus用于存储文档向量
Embedding模型: OpenAI或Ollama提供的文本向量化服务
Spring Boot: 后端服务框架
Spring Security: 系统安全认证

系统架构设计

@Configuration
@EnableAIAutomation
public class RAGSystemConfig {
    
    @Bean
    public VectorStore vectorStore() {
        return new MilvusVectorStore("localhost", 19530);
    }
    
    @Bean
    public EmbeddingModel embeddingModel() {
        return new OpenAiEmbeddingModel("your-api-key");
    }
    
    @Bean
    public DocumentLoader documentLoader() {
        return new CompositeDocumentLoader(
            new PdfDocumentLoader(),
            new WordDocumentLoader(),
            new TextDocumentLoader()
        );
    }
}

核心功能实现

1. 文档加载与处理

文档处理是整个系统的基础，我们需要支持多种格式的文档加载：

@Service
public class DocumentProcessingService {
    
    @Autowired
    private DocumentLoader documentLoader;
    
    @Autowired
    private EmbeddingModel embeddingModel;
    
    @Autowired
    private VectorStore vectorStore;
    
    public void processDocument(File documentFile) {
        // 加载文档
        Document document = documentLoader.load(documentFile);
        
        // 文本分块处理
        List<TextChunk> chunks = textSplitter.split(document.getContent());
        
        // 生成向量并存储
        chunks.forEach(chunk -> {
            float[] embedding = embeddingModel.embed(chunk.getText());
            vectorStore.storeVector(chunk.getId(), embedding, chunk.getMetadata());
        });
    }
}

2. 语义检索实现

基于向量相似度的语义检索是RAG系统的核心：

@Service
public class SemanticSearchService {
    
    @Autowired
    private VectorStore vectorStore;
    
    @Autowired
    private EmbeddingModel embeddingModel;
    
    public List<SearchResult> semanticSearch(String query, int topK) {
        // 将查询文本向量化
        float[] queryEmbedding = embeddingModel.embed(query);
        
        // 在向量数据库中搜索相似文档
        List<VectorSearchResult> results = vectorStore.search(
            queryEmbedding, 
            topK, 
            SearchParams.defaultParams()
        );
        
        return results.stream()
            .map(result -> new SearchResult(
                result.getId(),
                result.getScore(),
                result.getMetadata()
            ))
            .collect(Collectors.toList());
    }
}

3. 智能问答生成

结合检索结果生成准确的回答：

@Service
public class QAGenerationService {
    
    @Autowired
    private ChatModel chatModel;
    
    @Autowired
    private SemanticSearchService searchService;
    
    public String generateAnswer(String question) {
        // 检索相关文档片段
        List<SearchResult> relevantDocs = searchService.semanticSearch(question, 5);
        
        // 构建提示词
        String prompt = buildPrompt(question, relevantDocs);
        
        // 调用AI模型生成回答
        ChatResponse response = chatModel.generate(prompt);
        
        return response.getContent();
    }
    
    private String buildPrompt(String question, List<SearchResult> context) {
        StringBuilder promptBuilder = new StringBuilder();
        promptBuilder.append("基于以下文档内容，请回答用户的问题：\n\n");
        
        context.forEach(doc -> {
            promptBuilder.append("文档片段：").append(doc.getContent()).append("\n\n");
        });
        
        promptBuilder.append("用户问题：").append(question).append("\n\n");
        promptBuilder.append("请提供准确、简洁的回答，并注明答案的来源文档。");
        
        return promptBuilder.toString();
    }
}

高级特性实现

1. 多轮对话支持

通过会话内存实现多轮对话上下文维护：

@Service
public class ConversationService {
    
    @Autowired
    private ConversationMemory memory;
    
    @Autowired
    private QAGenerationService qaService;
    
    public String handleConversation(String sessionId, String userMessage) {
        // 获取会话历史
        List<ConversationTurn> history = memory.getConversationHistory(sessionId);
        
        // 构建包含上下文的查询
        String contextualQuery = buildContextualQuery(userMessage, history);
        
        // 生成回答
        String answer = qaService.generateAnswer(contextualQuery);
        
        // 保存到会话内存
        memory.addTurn(sessionId, userMessage, answer);
        
        return answer;
    }
    
    private String buildContextualQuery(String currentQuery, List<ConversationTurn> history) {
        if (history.isEmpty()) {
            return currentQuery;
        }
        
        StringBuilder contextBuilder = new StringBuilder();
        contextBuilder.append("对话历史：\n");
        
        history.forEach(turn -> {
            contextBuilder.append("用户：").append(turn.getUserMessage()).append("\n");
            contextBuilder.append("系统：").append(turn.getAssistantMessage()).append("\n");
        });
        
        contextBuilder.append("当前问题：").append(currentQuery);
        
        return contextBuilder.toString();
    }
}

2. 企业级安全控制

集成Spring Security实现访问控制：

@Configuration
@EnableWebSecurity
public class SecurityConfig {
    
    @Bean
    public SecurityFilterChain filterChain(HttpSecurity http) throws Exception {
        http
            .authorizeHttpRequests(authz -> authz
                .requestMatchers("/api/public/**").permitAll()
                .requestMatchers("/api/documents/upload").hasRole("ADMIN")
                .requestMatchers("/api/qa/**").authenticated()
                .anyRequest().authenticated()
            )
            .oauth2ResourceServer(OAuth2ResourceServerConfigurer::jwt)
            .sessionManagement(session -> session
                .sessionCreationPolicy(SessionCreationPolicy.STATELESS)
            );
        
        return http.build();
    }
}

性能优化策略

1. 向量检索优化

@Configuration
public class PerformanceConfig {
    
    @Bean
    public CacheManager cacheManager() {
        CaffeineCacheManager cacheManager = new CaffeineCacheManager();
        cacheManager.setCaffeine(Caffeine.newBuilder()
            .expireAfterWrite(10, TimeUnit.MINUTES)
            .maximumSize(1000));
        return cacheManager;
    }
    
    @Bean
    public AsyncTaskExecutor taskExecutor() {
        ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();
        executor.setCorePoolSize(10);
        executor.setMaxPoolSize(50);
        executor.setQueueCapacity(100);
        executor.setThreadNamePrefix("rag-executor-");
        executor.initialize();
        return executor;
    }
}

2. 批量处理优化

@Service
public class BatchProcessingService {
    
    @Async("taskExecutor")
    public CompletableFuture<Void> processDocumentsInBatch(List<File> documents) {
        return CompletableFuture.runAsync(() -> {
            documents.parallelStream().forEach(document -> {
                try {
                    processDocument(document);
                } catch (Exception e) {
                    log.error("处理文档失败: {}", document.getName(), e);
                }
            });
        });
    }
}

监控与运维

1. 系统监控配置

management:
  endpoints:
    web:
      exposure:
        include: health,metrics,prometheus
  metrics:
    tags:
      application: rag-system
  tracing:
    sampling:
      probability: 1.0

micrometer:
  observation:
    registry:
      step: 1m

2. 自定义监控指标

@Component
public class RAGMetrics {
    
    private final Counter queryCounter;
    private final Timer responseTimer;
    private final Gauge accuracyGauge;
    
    public RAGMetrics(MeterRegistry registry) {
        this.queryCounter = Counter.builder("rag.queries.total")
            .description("Total number of queries processed")
            .register(registry);
        
        this.responseTimer = Timer.builder("rag.response.time")
            .description("Time taken to generate responses")
            .register(registry);
    }
    
    public void recordQuery() {
        queryCounter.increment();
    }
    
    public Timer.Sample startTiming() {
        return Timer.start();
    }
    
    public void stopTiming(Timer.Sample sample) {
        sample.stop(responseTimer);
    }
}

实际应用场景

1. 企业知识库问答

系统可以对接企业的各类文档资源，包括产品手册、技术文档、政策文件等，为员工提供智能问答服务。

2. 客户服务自动化

集成到客服系统中，自动回答客户的常见问题，提高服务效率和质量。

3. 教育培训平台

用于在线教育平台，为学生提供个性化的学习辅导和问题解答。

部署与扩展

Docker容器化部署

FROM openjdk:17-jdk-slim

WORKDIR /app

COPY target/rag-system.jar app.jar
COPY config/application.yml config/

EXPOSE 8080

ENTRYPOINT ["java", "-jar", "app.jar", "--spring.config.location=config/application.yml"]

Kubernetes部署配置

apiVersion: apps/v1
kind: Deployment
metadata:
  name: rag-system
spec:
  replicas: 3
  selector:
    matchLabels:
      app: rag-system
  template:
    metadata:
      labels:
        app: rag-system
    spec:
      containers:
      - name: rag-app
        image: rag-system:latest
        ports:
        - containerPort: 8080
        resources:
          requests:
            memory: "1Gi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "1000m"
---
apiVersion: v1
kind: Service
metadata:
  name: rag-service
spec:
  selector:
    app: rag-system
  ports:
  - port: 80
    targetPort: 8080