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存在检索效率低、理解能力有限等问题。Spring AI结合RAG（Retrieval-Augmented Generation）技术，为企业提供了构建智能文档问答系统的强大工具。本文将深入探讨如何利用Spring AI框架和RAG技术构建高效的企业级文档问答系统。

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，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、Google AI、Ollama等模型
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技术原理

RAG（检索增强生成）技术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>0.8.0</version>
</dependency>
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-pgvector-store-spring-boot-starter</artifactId>
    <version>0.8.0</version>
</dependency>

文档处理模块

文档加载与分割

@Component
public class DocumentProcessor {
    
    @Autowired
    private TextSplitter textSplitter;
    
    public List<Document> processDocument(MultipartFile file) {
        try {
            String content = extractContent(file);
            return textSplitter.split(content);
        } catch (IOException e) {
            throw new RuntimeException("文档处理失败", e);
        }
    }
    
    private String extractContent(MultipartFile file) throws IOException {
        String filename = file.getOriginalFilename();
        if (filename.endsWith(".pdf")) {
            return PdfUtils.extractText(file.getInputStream());
        } else if (filename.endsWith(".docx")) {
            return DocxUtils.extractText(file.getInputStream());
        }
        return new String(file.getBytes(), StandardCharsets.UTF_8);
    }
}

向量化存储

@Configuration
public class VectorStoreConfig {
    
    @Bean
    public VectorStore vectorStore(EmbeddingModel embeddingModel) {
        return new PgVectorStore(
            embeddingModel,
            PgVectorStoreConfig.builder()
                .dimensions(1536) // OpenAI embedding dimensions
                .build()
        );
    }
}

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 = retrieveRelevantDocuments(question);
        
        // 2. 构建提示
        String context = buildContext(relevantDocs);
        Prompt prompt = buildPrompt(question, context);
        
        // 3. 生成回答
        ChatResponse response = chatClient.call(prompt);
        
        return response.getResult().getOutput().getContent();
    }
    
    private List<Document> retrieveRelevantDocuments(String question) {
        return vectorStore.similaritySearch(question, 5); // 检索最相关的5个文档
    }
    
    private String buildContext(List<Document> documents) {
        return documents.stream()
            .map(Document::getContent)
            .collect(Collectors.joining("\n\n"));
    }
    
    private Prompt buildPrompt(String question, String context) {
        String template = """
        基于以下上下文信息，请回答用户的问题。
        如果上下文中的信息不足以回答问题，请如实告知。
        
        上下文：
        {context}
        
        问题：{question}
        
        回答：
        """;
        
        return new Prompt(template, Map.of(
            "context", context,
            "question", question
        ));
    }
}

API接口设计

REST控制器

@RestController
@RequestMapping("/api/rag")
public class RagController {
    
    @Autowired
    private RagService ragService;
    
    @Autowired
    private DocumentProcessor documentProcessor;
    
    @Autowired
    private VectorStore vectorStore;
    
    @PostMapping("/upload")
    public ResponseEntity<String> uploadDocument(@RequestParam("file") MultipartFile file) {
        List<Document> documents = documentProcessor.processDocument(file);
        vectorStore.add(documents);
        return ResponseEntity.ok("文档上传成功");
    }
    
    @PostMapping("/ask")
    public ResponseEntity<String> askQuestion(@RequestBody QuestionRequest request) {
        String answer = ragService.answerQuestion(request.getQuestion());
        return ResponseEntity.ok(answer);
    }
    
    @GetMapping("/documents/count")
    public ResponseEntity<Long> getDocumentCount() {
        long count = vectorStore.getDocumentCount();
        return ResponseEntity.ok(count);
    }
}

@Data
class QuestionRequest {
    private String question;
}

高级特性实现

多模态文档支持

@Component
public class MultiModalProcessor {
    
    public List<Document> processImageDocument(MultipartFile imageFile) {
        // 使用OCR技术提取图片中的文本
        String extractedText = ocrService.extractText(imageFile);
        return textSplitter.split(extractedText);
    }
    
    public List<Document> processAudioDocument(MultipartFile audioFile) {
        // 使用语音识别技术转换音频为文本
        String transcribedText = speechToTextService.transcribe(audioFile);
        return textSplitter.split(transcribedText);
    }
}

智能路由与缓存

@Service
public class IntelligentRouter {
    
    @Autowired
    private CacheManager cacheManager;
    
    public String routeQuestion(String question) {
        // 检查缓存
        String cachedAnswer = checkCache(question);
        if (cachedAnswer != null) {
            return cachedAnswer;
        }
        
        // 根据问题类型选择不同的处理策略
        QuestionType type = classifyQuestion(question);
        
        switch (type) {
            case FACTUAL:
                return factualQuestionService.answer(question);
            case ANALYTICAL:
                return analyticalQuestionService.answer(question);
            case COMPARATIVE:
                return comparativeQuestionService.answer(question);
            default:
                return ragService.answerQuestion(question);
        }
    }
    
    private QuestionType classifyQuestion(String question) {
        // 使用机器学习模型进行问题分类
        // 实现省略
        return QuestionType.GENERAL;
    }
}

性能优化策略

向量索引优化

@Configuration
public class PerformanceConfig {
    
    @Bean
    public VectorStore optimizedVectorStore(EmbeddingModel embeddingModel) {
        return new PgVectorStore(
            embeddingModel,
            PgVectorStoreConfig.builder()
                .dimensions(1536)
                .indexType("IVFFLAT") // 使用IVF扁平索引
                .indexLists(100) // 索引列表数量
                .build()
        );
    }
}

批量处理与异步操作

@Service
public class BatchProcessingService {
    
    @Async
    public CompletableFuture<Void> batchAddDocuments(List<Document> documents) {
        return CompletableFuture.runAsync(() -> {
            vectorStore.add(documents);
        });
    }
    
    @Async
    public CompletableFuture<List<String>> batchAnswerQuestions(List<String> questions) {
        return CompletableFuture.supplyAsync(() -> {
            return questions.stream()
                .map(ragService::answerQuestion)
                .collect(Collectors.toList());
        });
    }
}

监控与运维

性能指标收集

@Component
public class MetricsCollector {
    
    private final MeterRegistry meterRegistry;
    
    public MetricsCollector(MeterRegistry meterRegistry) {
        this.meterRegistry = meterRegistry;
    }
    
    public void recordQueryTime(String question, long duration) {
        Timer.builder("rag.query.time")
            .tag("question_length", String.valueOf(question.length()))
            .register(meterRegistry)
            .record(duration, TimeUnit.MILLISECONDS);
    }
    
    public void recordCacheHit(boolean hit) {
        Counter.builder("rag.cache.hits")
            .tag("hit", String.valueOf(hit))
            .register(meterRegistry)
            .increment();
    }
}

健康检查端点

@Component
public class HealthIndicator implements org.springframework.boot.actuate.health.HealthIndicator {
    
    @Autowired
    private VectorStore vectorStore;
    
    @Override
    public Health health() {
        try {
            long documentCount = vectorStore.getDocumentCount();
            return Health.up()
                .withDetail("document_count", documentCount)
                .withDetail("store_type", vectorStore.getClass().getSimpleName())
                .build();
        } catch (Exception e) {
            return Health.down(e).build();
        }
    }
}

安全考虑

输入验证与过滤

@Component
public class InputValidator {
    
    public void validateQuestion(String question) {
        if (question == null || question.trim().isEmpty()) {
            throw new IllegalArgumentException("问题不能为空");
        }
        
        if (question.length() > 1000) {
            throw new IllegalArgumentException("问题长度超过限制");
        }
        
        // 防止注入攻击
        if (containsMaliciousContent(question)) {
            throw new SecurityException("检测到恶意输入");
        }
    }
    
    private boolean containsMaliciousContent(String input) {
        // 实现恶意内容检测逻辑
        return false;
    }
}

访问控制

@Configuration
@EnableWebSecurity
public class SecurityConfig {
    
    @Bean
    public SecurityFilterChain filterChain(HttpSecurity http) throws Exception {
        return http
            .authorizeHttpRequests(auth -> auth
                .requestMatchers("/api/rag/ask").authenticated()
                .requestMatchers("/api/rag/upload").hasRole("ADMIN")
                .anyRequest().permitAll()
            )
            .oauth2ResourceServer(OAuth2ResourceServerConfigurer::jwt)
            .build();
    }
}

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

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: "1000m"
---
apiVersion: v1
kind: Service
metadata:
  name: rag-service
spec:
  selector:
    app: rag-system
  ports:
  - port: 80
    targetPort: 8080