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基础的检索功能，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。

技术栈概述

核心组件

• Spring AI: Spring生态系统中的AI集成框架
• RAG架构: 检索增强生成技术栈
• 向量数据库: Milvus/Chroma/Redis
• Embedding模型: OpenAI/Ollama等
• Spring Boot: 后端服务框架

系统架构设计

整体架构

用户请求 → API网关 → Spring AI服务 → RAG引擎 → 向量数据库 → 返回结果

核心模块

1. 文档预处理模块
2. 向量化处理模块
3. 语义检索模块
4. 答案生成模块
5. 会话管理模块

实现步骤详解

1. 环境搭建与依赖配置

首先在Spring Boot项目中添加Spring AI依赖：

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-core</artifactId>
    <version>0.8.1</version>
</dependency>

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

2. 文档加载与预处理

实现文档加载器，支持多种格式：

@Component
public class DocumentLoader {
    
    @Autowired
    private TextSplitter textSplitter;
    
    public List<Document> loadDocuments(String filePath) {
        // 支持PDF、Word、TXT等多种格式
        List<Document> documents = new ArrayList<>();
        
        // 文件解析逻辑
        String content = parseFileContent(filePath);
        
        // 文本分割
        List<String> chunks = textSplitter.splitText(content);
        
        for (String chunk : chunks) {
            documents.add(new Document(chunk, Map.of("source", filePath)));
        }
        
        return documents;
    }
    
    private String parseFileContent(String filePath) {
        // 具体的文件解析实现
        return "";
    }
}

3. 向量化处理

使用Embedding模型将文本转换为向量：

@Service
public class EmbeddingService {
    
    @Autowired
    private EmbeddingModel embeddingModel;
    
    public List<Double> generateEmbedding(String text) {
        return embeddingModel.embed(text);
    }
    
    public List<List<Double>> generateEmbeddings(List<String> texts) {
        return embeddingModel.embed(texts);
    }
}

4. 向量数据库集成

集成Milvus向量数据库：

@Configuration
public class VectorStoreConfig {
    
    @Value("${milvus.host}")
    private String milvusHost;
    
    @Value("${milvus.port}")
    private int milvusPort;
    
    @Bean
    public MilvusService milvusService() {
        ConnectParam connectParam = ConnectParam.newBuilder()
            .withHost(milvusHost)
            .withPort(milvusPort)
            .build();
        
        return new MilvusService(connectParam);
    }
    
    @Bean
    public VectorStore vectorStore(MilvusService milvusService, 
                                  EmbeddingService embeddingService) {
        return new MilvusVectorStore(milvusService, embeddingService);
    }
}

5. RAG引擎实现

核心的检索增强生成逻辑：

@Service
public class RAGService {
    
    @Autowired
    private VectorStore vectorStore;
    
    @Autowired
    private ChatClient chatClient;
    
    @Autowired
    private EmbeddingService embeddingService;
    
    public String answerQuestion(String question, String conversationId) {
        // 1. 生成问题向量
        List<Double> questionEmbedding = embeddingService.generateEmbedding(question);
        
        // 2. 向量检索相似文档
        List<Document> relevantDocs = vectorStore.similaritySearch(
            questionEmbedding, 5, 0.7);
        
        // 3. 构建提示词
        String context = buildContext(relevantDocs);
        String prompt = buildPrompt(question, context);
        
        // 4. 调用AI模型生成答案
        ChatResponse response = chatClient.generate(prompt);
        
        // 5. 保存会话历史
        saveConversation(conversationId, question, response.getText());
        
        return response.getText();
    }
    
    private String buildContext(List<Document> documents) {
        StringBuilder context = new StringBuilder();
        for (Document doc : documents) {
            context.append(doc.getContent()).append("\n\n");
        }
        return context.toString();
    }
    
    private String buildPrompt(String question, String context) {
        return String.format("""
            基于以下上下文信息，请回答用户的问题。
            如果上下文信息不足以回答问题，请如实告知。
            
            上下文：
            %s
            
            问题：%s
            
            请提供准确、简洁的回答：
            """, context, question);
    }
}

6. REST API设计

提供问答接口：

@RestController
@RequestMapping("/api/rag")
public class RAGController {
    
    @Autowired
    private RAGService ragService;
    
    @PostMapping("/ask")
    public ResponseEntity<AnswerResponse> askQuestion(
            @RequestBody QuestionRequest request,
            @RequestHeader(value = "X-Conversation-Id", required = false) String conversationId) {
        
        if (conversationId == null) {
            conversationId = UUID.randomUUID().toString();
        }
        
        String answer = ragService.answerQuestion(request.getQuestion(), conversationId);
        
        AnswerResponse response = new AnswerResponse();
        response.setAnswer(answer);
        response.setConversationId(conversationId);
        response.setTimestamp(LocalDateTime.now());
        
        return ResponseEntity.ok(response);
    }
    
    @PostMapping("/documents")
    public ResponseEntity<String> uploadDocument(@RequestParam("file") MultipartFile file) {
        try {
            // 文档上传和处理逻辑
            String filePath = saveUploadedFile(file);
            // 处理文档并存入向量数据库
            processDocument(filePath);
            
            return ResponseEntity.ok("文档上传成功");
        } catch (Exception e) {
            return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR)
                .body("文档上传失败: " + e.getMessage());
        }
    }
}

高级特性实现

1. 会话内存管理

实现多轮对话上下文保持：

@Service
public class ConversationService {
    
    @Autowired
    private RedisTemplate<String, Object> redisTemplate;
    
    private static final String CONVERSATION_PREFIX = "conv:";
    private static final long TTL = 3600; // 1小时过期
    
    public void saveConversation(String conversationId, String question, String answer) {
        Conversation conversation = getConversation(conversationId);
        if (conversation == null) {
            conversation = new Conversation(conversationId);
        }
        
        conversation.addMessage(new Message("user", question));
        conversation.addMessage(new Message("assistant", answer));
        
        redisTemplate.opsForValue().set(
            CONVERSATION_PREFIX + conversationId, 
            conversation, 
            TTL, TimeUnit.SECONDS
        );
    }
    
    public Conversation getConversation(String conversationId) {
        return (Conversation) redisTemplate.opsForValue()
            .get(CONVERSATION_PREFIX + conversationId);
    }
}

2. 智能代理（Agent）集成

实现复杂的多步骤任务处理：

@Service
public class AgentService {
    
    @Autowired
    private ToolExecutionFramework toolExecutionFramework;
    
    public String executeComplexTask(String taskDescription, String conversationId) {
        // 1. 任务分解
        List<SubTask> subTasks = decomposeTask(taskDescription);
        
        // 2. 按顺序执行子任务
        StringBuilder result = new StringBuilder();
        for (SubTask subTask : subTasks) {
            String subResult = executeSubTask(subTask, conversationId);
            result.append(subResult).append("\n");
        }
        
        return result.toString();
    }
    
    private List<SubTask> decomposeTask(String task) {
        // 使用AI模型进行任务分解
        // 返回任务步骤列表
        return new ArrayList<>();
    }
}

3. 防止AI幻觉（Hallucination）

实现答案验证机制：

@Component
public class HallucinationChecker {
    
    @Autowired
    private VectorStore vectorStore;
    
    @Autowired
    private EmbeddingService embeddingService;
    
    public boolean checkAnswer(String answer, String originalQuestion) {
        // 1. 检查答案是否基于检索到的上下文
        double similarityScore = calculateSimilarity(answer, originalQuestion);
        
        // 2. 检查答案中是否包含无法验证的信息
        boolean containsUnverifiableInfo = containsUnverifiableInformation(answer);
        
        return similarityScore > 0.6 && !containsUnverifiableInfo;
    }
    
    private double calculateSimilarity(String text1, String text2) {
        List<Double> embedding1 = embeddingService.generateEmbedding(text1);
        List<Double> embedding2 = embeddingService.generateEmbedding(text2);
        
        return cosineSimilarity(embedding1, embedding2);
    }
}

性能优化策略

1. 向量检索优化

@Configuration
public class PerformanceConfig {
    
    @Bean
    public HikariDataSource dataSource() {
        HikariConfig config = new HikariConfig();
        config.setJdbcUrl("jdbc:mysql://localhost:3306/rag_system");
        config.setUsername("username");
        config.setPassword("password");
        config.setMaximumPoolSize(20);
        config.setMinimumIdle(5);
        config.setConnectionTimeout(30000);
        config.setIdleTimeout(600000);
        config.setMaxLifetime(1800000);
        
        return new HikariDataSource(config);
    }
    
    @Bean
    public CacheManager cacheManager() {
        CaffeineCacheManager cacheManager = new CaffeineCacheManager();
        cacheManager.setCaffeine(Caffeine.newBuilder()
            .expireAfterWrite(10, TimeUnit.MINUTES)
            .maximumSize(1000));
        return cacheManager;
    }
}

2. 异步处理优化

使用Spring的异步处理提高响应速度：

@Service
public class AsyncDocumentProcessor {
    
    @Async
    public CompletableFuture<Void> processDocumentAsync(String filePath) {
        return CompletableFuture.runAsync(() -> {
            try {
                // 耗时的文档处理操作
                processDocument(filePath);
            } catch (Exception e) {
                log.error("文档处理失败: {}", filePath, e);
            }
        });
    }
}

监控与运维

1. 集成Prometheus监控

@Configuration
public class MonitoringConfig {
    
    @Bean
    public MeterRegistryCustomizer<MeterRegistry> metricsCommonTags() {
        return registry -> registry.config().commonTags(
            "application", "rag-system",
            "environment", "production"
        );
    }
    
    @Bean
    public TimedAspect timedAspect(MeterRegistry registry) {
        return new TimedAspect(registry);
    }
}

2. 日志记录策略

@Aspect
@Component
@Slf4j
public class LoggingAspect {
    
    @Around("execution(* com.example.rag.service.*.*(..))")
    public Object logServiceMethods(ProceedingJoinPoint joinPoint) throws Throwable {
        String methodName = joinPoint.getSignature().getName();
        Object[] args = joinPoint.getArgs();
        
        log.info("方法调用: {} 参数: {}", methodName, Arrays.toString(args));
        
        long startTime = System.currentTimeMillis();
        Object result = joinPoint.proceed();
        long endTime = System.currentTimeMillis();
        
        log.info("方法完成: {} 耗时: {}ms", methodName, (endTime - startTime));
        
        return result;
    }
}

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

EXPOSE 8080

ENV JAVA_OPTS="-Xms512m -Xmx1024m"

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

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: "1"
---
apiVersion: v1
kind: Service
metadata:
  name: rag-service
spec:
  selector:
    app: rag-system
  ports:
  - port: 80
    targetPort: 8080

总结与展望

本文详细介绍了基于Spring AI和RAG技术构建企业级智能文档问答系统的完整方案。通过结合先进的AI技术和成熟的Java生态系统，我们能够构建出既强大又稳定的智能问答系统。

关键优势

1. 准确性高: RAG架构确保答案基于真实文档内容
2. 扩展性强: 微服务架构支持水平扩展
3. 维护简单: Spring生态提供完善的工具链
4. 成本可控: 开源技术栈降低总体拥有成本

未来发展方向

1. 多模态文档支持（图片、表格等）
2. 实时文档更新与增量索引
3. 多语言支持与跨语言检索
4. 个性化答案生成与用户偏好学习

通过本文的实践指南，开发者可以快速构建属于自己的智能文档问答系统，为企业数字化转型提供强有力的技术支撑。