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

在人工智能技术飞速发展的今天，如何将AI能力有效集成到企业应用中成为了技术团队面临的重要挑战。Spring AI作为Spring生态系统中的AI集成框架，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强大的技术支撑。本文将深入探讨如何使用Spring AI和RAG技术构建高效、准确的企业级智能问答系统。

技术架构概述

Spring AI框架

Spring AI是Spring生态系统中的AI集成框架，它提供了统一的API来访问各种AI模型和服务。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机制
向量化集成：支持多种向量数据库和嵌入模型
工具调用框架：标准化AI代理的工具执行能力

RAG技术原理

RAG（Retrieval-Augmented Generation）是一种结合信息检索和文本生成的技术架构：

文档处理：将企业文档进行分块、向量化处理
语义检索：根据用户问题检索最相关的文档片段
上下文增强：将检索到的信息作为上下文提供给AI模型
智能生成：AI模型基于上下文生成准确回答

系统设计与实现

环境准备

首先配置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-pgvector-store-spring-boot-starter</artifactId>
    <version>0.8.1</version>
</dependency>

文档处理模块

文档加载与分块

@Service
public class DocumentProcessor {
    
    @Autowired
    private TextSplitter textSplitter;
    
    public List<Document> processDocument(String filePath) {
        // 加载文档
        DocumentLoader loader = new FileDocumentLoader();
        Document document = loader.load(filePath);
        
        // 文档分块
        return textSplitter.split(document);
    }
}

向量化存储

@Configuration
public class VectorStoreConfig {
    
    @Bean
    public VectorStore vectorStore(EmbeddingModel embeddingModel) {
        return new PgVectorStore(
            embeddingModel,
            new PgVectorStore.PgVectorStoreConfig()
        );
    }
    
    @Bean
    public EmbeddingModel embeddingModel() {
        return new OpenAiEmbeddingModel(
            new OpenAiApi("your-api-key")
        );
    }
}

检索增强生成服务

RAG服务实现

@Service
public class RagService {
    
    @Autowired
    private VectorStore vectorStore;
    
    @Autowired
    private ChatClient chatClient;
    
    public String answerQuestion(String question) {
        // 1. 检索相关文档
        List<Document> relevantDocs = vectorStore.similaritySearch(question);
        
        // 2. 构建提示
        String context = buildContext(relevantDocs);
        String prompt = buildPrompt(question, context);
        
        // 3. 生成回答
        return chatClient.generate(prompt);
    }
    
    private String buildContext(List<Document> documents) {
        return documents.stream()
            .map(Document::getContent)
            .collect(Collectors.joining("\n\n"));
    }
    
    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。
            
            上下文：
            %s
            
            问题：%s
            
            要求：回答要准确、简洁，基于提供的上下文信息。
            """, context, question);
    }
}

API控制器

@RestController
@RequestMapping("/api/rag")
public class RagController {
    
    @Autowired
    private RagService ragService;
    
    @PostMapping("/ask")
    public ResponseEntity<String> askQuestion(@RequestBody QuestionRequest request) {
        try {
            String answer = ragService.answerQuestion(request.getQuestion());
            return ResponseEntity.ok(answer);
        } catch (Exception e) {
            return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR)
                .body("处理问题时发生错误：" + e.getMessage());
        }
    }
    
    @PostMapping("/ingest")
    public ResponseEntity<String> ingestDocument(@RequestParam("file") MultipartFile file) {
        try {
            // 文档处理逻辑
            return ResponseEntity.ok("文档处理完成");
        } catch (Exception e) {
            return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR)
                .body("文档处理失败：" + e.getMessage());
        }
    }
}

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 continueConversation(String sessionId, String userMessage) {
        List<ChatMessage> history = conversationHistory.getOrDefault(sessionId, new ArrayList<>());
        
        // 添加上下文到问题中
        String contextualQuestion = buildContextualQuestion(userMessage, history);
        
        String answer = ragService.answerQuestion(contextualQuestion);
        
        // 更新对话历史
        history.add(new ChatMessage("user", userMessage));
        history.add(new ChatMessage("assistant", answer));
        conversationHistory.put(sessionId, history);
        
        return answer;
    }
    
    private String buildContextualQuestion(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

@Service
public class IntelligentRouter {
    
    @Autowired
    private RagService ragService;
    
    @Autowired
    private FaqService faqService;
    
    @Autowired
    private FallbackService fallbackService;
    
    public String routeQuestion(String question) {
        // 1. 检查是否是常见问题
        if (faqService.isFaq(question)) {
            return faqService.getAnswer(question);
        }
        
        try {
            // 2. 使用RAG处理复杂问题
            return ragService.answerQuestion(question);
        } catch (Exception e) {
            // 3. 降级处理
            return fallbackService.handleFallback(question);
        }
    }
}

性能优化与监控

缓存策略

@Configuration
@EnableCaching
public class CacheConfig {
    
    @Bean
    public CacheManager cacheManager() {
        CaffeineCacheManager cacheManager = new CaffeineCacheManager();
        cacheManager.setCaffeine(Caffeine.newBuilder()
            .expireAfterWrite(10, TimeUnit.MINUTES)
            .maximumSize(1000));
        return cacheManager;
    }
}

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

监控指标

@Component
public class RagMetrics {
    
    private final MeterRegistry meterRegistry;
    
    public RagMetrics(MeterRegistry meterRegistry) {
        this.meterRegistry = meterRegistry;
    }
    
    public void recordResponseTime(long milliseconds) {
        meterRegistry.timer("rag.response.time")
            .record(milliseconds, TimeUnit.MILLISECONDS);
    }
    
    public void recordSuccess() {
        meterRegistry.counter("rag.requests.success").increment();
    }
    
    public void recordFailure() {
        meterRegistry.counter("rag.requests.failure").increment();
    }
}

部署与运维

Docker容器化

FROM openjdk:17-jdk-slim

WORKDIR /app

COPY target/rag-system.jar app.jar

EXPOSE 8080

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