Spring AI与RAG技术实战：构建企业级智能文档问答系统

最新推荐文章于 2025-11-23 19:13:18 发布

原创最新推荐文章于 2025-11-23 19:13:18 发布 · 850 阅读

8 ·

CC 4.0 BY-SA版权

文章标签：

#Spring AI #RAG #智能问答 #企业级应用 #Java #人工智能

Java 专栏收录该内容

873 篇文章

订阅专栏

Spring AI与RAG技术实战：构建企业级智能文档问答系统

引言

在人工智能技术飞速发展的今天，企业面临着海量文档管理和知识检索的挑战。传统的基于关键词的搜索方式已经无法满足用户对精准、智能问答的需求。Spring AI结合RAG（检索增强生成）技术为企业提供了构建智能文档问答系统的完美解决方案。本文将深入探讨如何使用Spring AI框架和RAG技术构建高效的企业级智能问答系统。

技术栈概述

Spring AI框架

Spring AI是Spring生态系统中的AI集成框架，提供了统一的API来访问各种AI模型和服务。其主要特性包括：

模型抽象层：统一访问OpenAI、Azure OpenAI、Google AI等主流AI服务
提示工程支持：内置提示模板和变量替换功能
向量化集成：支持多种向量数据库和嵌入模型
工具调用标准化：提供统一的工具执行框架

RAG技术架构

RAG（Retrieval-Augmented Generation）结合了信息检索和文本生成的优势：

检索阶段：从知识库中检索与问题相关的文档片段
增强阶段：将检索到的信息作为上下文提供给生成模型
生成阶段：基于检索到的上下文生成准确、可靠的回答

系统架构设计

整体架构

用户界面层 → API网关层 → 业务逻辑层 → 数据访问层
    ↓           ↓           ↓           ↓
Web前端    Spring Boot   Spring AI   向量数据库
           ↓           ↓           ↓
       安全认证      RAG引擎     文档存储

核心组件

文档处理管道
- 文档加载与解析
- 文本分块与向量化
- 元数据提取与索引
检索增强模块
- 语义相似度计算
- 相关性排序
- 上下文构建
生成推理模块
- 提示工程优化
- 模型调用管理
- 响应后处理

实战开发步骤

环境准备

首先添加Spring AI依赖：

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
    <version>0.8.1</version>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-transformers-spring-boot-starter</artifactId>
    <version>0.8.1</version>
</dependency>

文档处理实现

@Service
public class DocumentProcessor {
    
    @Autowired
    private EmbeddingClient embeddingClient;
    
    public List<DocumentChunk> processDocument(String content) {
        // 文本分块
        List<String> chunks = textSplitter.split(content);
        
        // 向量化处理
        List<Embedding> embeddings = embeddingClient.embed(chunks);
        
        return createDocumentChunks(chunks, embeddings);
    }
    
    private List<DocumentChunk> createDocumentChunks(
        List<String> chunks, List<Embedding> embeddings) {
        
        List<DocumentChunk> documentChunks = new ArrayList<>();
        for (int i = 0; i < chunks.size(); i++) {
            DocumentChunk chunk = new DocumentChunk(
                chunks.get(i),
                embeddings.get(i).getVector(),
                extractMetadata(chunks.get(i))
            );
            documentChunks.add(chunk);
        }
        return documentChunks;
    }
}

向量数据库集成

使用Redis作为向量数据库：

@Configuration
public class VectorStoreConfig {
    
    @Bean
    public VectorStore redisVectorStore(
        RedisConnectionFactory connectionFactory,
        EmbeddingClient embeddingClient) {
        
        RedisVectorStore.RedisVectorStoreConfig config = 
            RedisVectorStore.RedisVectorStoreConfig.builder()
                .withIndexName("document-index")
                .withMetadataFields(Set.of("title", "category", "timestamp"))
                .build();
        
        return new RedisVectorStore(config, connectionFactory, embeddingClient);
    }
}

RAG服务实现

@Service
public class RagService {
    
    @Autowired
    private ChatClient chatClient;
    
    @Autowired
    private VectorStore vectorStore;
    
    public String answerQuestion(String question) {
        // 1. 检索相关文档
        List<Document> relevantDocs = retrieveRelevantDocuments(question);
        
        // 2. 构建提示上下文
        String context = buildContext(relevantDocs);
        
        // 3. 生成回答
        return generateAnswer(question, context);
    }
    
    private List<Document> retrieveRelevantDocuments(String question) {
        // 使用问题向量进行相似度搜索
        return vectorStore.similaritySearch(
            SearchRequest.query(question).withTopK(5)
        );
    }
    
    private String generateAnswer(String question, String context) {
        PromptTemplate promptTemplate = new PromptTemplate("""
            你是一个专业的文档问答助手。请基于以下上下文信息回答问题。
            
            上下文：
            {context}
            
            问题：{question}
            
            要求：
            1. 回答要准确、简洁
            2. 如果上下文没有相关信息，请如实告知
            3. 不要编造信息
            """);
        
        Prompt prompt = promptTemplate.create(
            Map.of("context", context, "question", question)
        );
        
        return chatClient.call(prompt).getResult().getOutput().getContent();
    }
}

高级特性实现

多轮对话支持

@Service
public class ConversationService {
    
    @Autowired
    private RagService ragService;
    
    private final Map<String, List<ChatMessage>> conversationHistory = 
        new ConcurrentHashMap<>();
    
    public String handleConversation(String sessionId, String userMessage) {
        // 获取对话历史
        List<ChatMessage> history = conversationHistory
            .getOrDefault(sessionId, new ArrayList<>());
        
        // 添加上下文到当前问题
        String enhancedQuestion = enhanceQuestionWithHistory(userMessage, history);
        
        // 获取回答
        String answer = ragService.answerQuestion(enhancedQuestion);
        
        // 更新对话历史
        updateConversationHistory(sessionId, userMessage, answer);
        
        return answer;
    }
    
    private String enhanceQuestionWithHistory(
        String currentQuestion, List<ChatMessage> history) {
        
        if (history.isEmpty()) {
            return currentQuestion;
        }
        
        StringBuilder contextBuilder = new StringBuilder();
        contextBuilder.append("之前的对话上下文：\n");
        
        for (ChatMessage message : history) {
            contextBuilder.append(message.getRole())
                         .append(": ")
                         .append(message.getContent())
                         .append("\n");
        }
        
        contextBuilder.append("当前问题：")
                     .append(currentQuestion);
        
        return contextBuilder.toString();
    }
}

智能路由与降级策略

@Component
public class IntelligentRouter {
    
    @Autowired
    private RagService ragService;
    
    @Autowired
    private FaqService faqService;
    
    @Autowired
    private KeywordSearchService keywordSearchService;
    
    public String routeQuestion(String question) {
        // 分析问题类型和复杂度
        QuestionAnalysis analysis = analyzeQuestion(question);
        
        if (analysis.isSimpleFaq()) {
            // 简单FAQ问题，使用快速检索
            return faqService.findAnswer(question);
        } else if (analysis.requiresSemanticSearch()) {
            // 复杂问题，使用RAG
            return ragService.answerQuestion(question);
        } else {
            // 降级到关键词搜索
            return keywordSearchService.search(question);
        }
    }
    
    private QuestionAnalysis analyzeQuestion(String question) {
        // 实现问题分析逻辑
        return new QuestionAnalysis(question);
    }
}

性能优化策略

缓存机制

@Configuration
@EnableCaching
public class CacheConfig {
    
    @Bean
    public CacheManager cacheManager() {
        CaffeineCacheManager cacheManager = new CaffeineCacheManager();
        cacheManager.setCaffeine(Caffeine.newBuilder()
            .expireAfterWrite(30, TimeUnit.MINUTES)
            .maximumSize(1000));
        return cacheManager;
    }
}

@Service
public class CachedRagService {
    
    @Autowired
    private RagService ragService;
    
    @Cacheable(value = "answers", key = "#question")
    public String getCachedAnswer(String question) {
        return ragService.answerQuestion(question);
    }
}

批量处理优化

@Service
public class BatchDocumentProcessor {
    
    @Async("documentProcessingExecutor")
    public CompletableFuture<Void> processDocumentsInBatch(
        List<String> documents) {
        
        return CompletableFuture.runAsync(() -> {
            documents.parallelStream()
                .forEach(this::processSingleDocument);
        });
    }
    
    private void processSingleDocument(String content) {
        // 文档处理逻辑
    }
}

@Configuration
@EnableAsync
public class AsyncConfig {
    
    @Bean("documentProcessingExecutor")
    public TaskExecutor documentProcessingExecutor() {
        ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();
        executor.setCorePoolSize(4);
        executor.setMaxPoolSize(8);
        executor.setQueueCapacity(100);
        executor.setThreadNamePrefix("doc-process-");
        executor.initialize();
        return executor;
    }
}

监控与运维

指标收集

@Component
public class RagMetrics {
    
    private final MeterRegistry meterRegistry;
    
    public RagMetrics(MeterRegistry meterRegistry) {
        this.meterRegistry = meterRegistry;
    }
    
    @EventListener
    public void handleQuestionEvent(QuestionAnsweredEvent event) {
        // 记录响应时间
        Timer.Sample sample = Timer.start(meterRegistry);
        
        // 处理完成后记录
        sample.stop(meterRegistry.timer("rag.response.time"));
        
        // 记录成功率
        Counter.builder("rag.requests.total")
            .tag("status", event.isSuccess() ? "success" : "failure")
            .register(meterRegistry)
            .increment();
    }
}

健康检查

@Component
public class RagHealthIndicator implements HealthIndicator {
    
    @Autowired
    private VectorStore vectorStore;
    
    @Autowired
    private ChatClient chatClient;
    
    @Override
    public Health health() {
        try {
            // 检查向量数据库连接
            vectorStore.similaritySearch(
                SearchRequest.query("test").withTopK(1)
            );
            
            // 检查AI服务连接
            chatClient.call("Hello");
            
            return Health.up().build();
        } catch (Exception e) {
            return Health.down(e).build();
        }
    }
}

安全考虑

数据隐私保护

@Service
public class PrivacyAwareRagService {
    
    @Autowired
    private RagService ragService;
    
    @Autowired
    private DataMaskingService maskingService;
    
    public String answerQuestionSafely(String question, User user) {
        // 脱敏处理
        String sanitizedQuestion = maskingService.maskSensitiveData(question);
        
        // 权限检查
        if (!hasAccessToDocuments(user, sanitizedQuestion)) {
            throw new AccessDeniedException("无权访问相关文档");
        }
        
        String answer = ragService.answerQuestion(sanitizedQuestion);
        
        // 回答后处理
        return maskingService.maskSensitiveData(answer);
    }
    
    private boolean hasAccessToDocuments(User user, String question) {
        // 实现权限检查逻辑
        return true; // 简化实现
    }
}

部署与扩展

Docker容器化

FROM openjdk:17-jdk-slim

WORKDIR /app

COPY target/rag-system.jar app.jar

EXPOSE 8080

ENV SPRING_PROFILES_ACTIVE=prod
ENV JAVA_OPTS="-Xmx512m -Xms256m"

ENTRYPOINT ["java", "-jar", "app.jar"]

Kubernetes部署

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: "512Mi"
            cpu: "250m"
          limits:
            memory: "1Gi"
            cpu: "500m"
        env:
        - name: SPRING_PROFILES_ACTIVE
          value: "prod"
---
apiVersion: v1
kind: Service
metadata:
  name: rag-service
spec:
  selector:
    app: rag-system
  ports:
  - port: 80
    targetPort: 8080
  type: LoadBalancer