Spring AI与RAG技术实战：构建企业级智能文档问答系统

原创于 2025-08-28 09:02:37 发布 · 432 阅读

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#Spring AI #RAG #Java #Spring Boot #AI集成 #向量数据库 #智能问答

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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利用Spring AI和RAG技术构建高效、准确的企业级智能问答系统。

技术栈概述

Spring AI框架

Spring AI是Spring官方推出的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
工具调用标准化：提供统一的函数调用接口

RAG技术架构

RAG（Retrieval-Augmented Generation）是一种结合检索和生成的AI技术，通过以下步骤工作：

文档处理：将企业文档进行分块、向量化处理
语义检索：根据用户问题检索相关文档片段
上下文增强：将检索结果作为上下文提供给AI模型
生成回答：AI模型基于上下文生成准确回答

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
                                   ↓
                               向量数据库
                                   ↓
                               文档存储

技术组件选型

Web框架：Spring Boot 3.x + Spring WebFlux
AI服务：Spring AI + OpenAI GPT-4
向量数据库：Redis Vector Search
文档处理：Apache Tika + LangChain4j
缓存：Redis + Caffeine
监控：Micrometer + Prometheus

核心实现步骤

1. 环境配置与依赖

首先在pom.xml中添加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-redis-spring-boot-starter</artifactId>
    <version>0.8.1</version>
</dependency>

2. 文档处理与向量化

实现文档加载和分块处理：

@Service
public class DocumentProcessor {
    
    @Autowired
    private EmbeddingClient embeddingClient;
    
    @Autowired
    private VectorStore vectorStore;
    
    public void processDocument(MultipartFile file) {
        // 文档解析
        String content = parseDocument(file);
        
        // 文本分块
        List<TextSegment> segments = splitText(content);
        
        // 向量化并存储
        segments.forEach(segment -> {
            List<Double> embedding = embeddingClient.embed(segment.text());
            vectorStore.add(
                List.of(new Document(
                    segment.text(),
                    Map.of("source", file.getOriginalFilename()),
                    embedding
                ))
            );
        });
    }
    
    private List<TextSegment> splitText(String content) {
        // 实现文本分块逻辑
        return TextSplitter.recursiveSplit(content, 1000, 200);
    }
}

3. 语义检索实现

构建基于向量相似度的检索系统：

@Service
public class SemanticSearchService {
    
    @Autowired
    private VectorStore vectorStore;
    
    @Autowired
    private EmbeddingClient embeddingClient;
    
    public List<Document> searchRelevantDocuments(String query, int topK) {
        // 查询向量化
        List<Double> queryEmbedding = embeddingClient.embed(query);
        
        // 相似度搜索
        return vectorStore.similaritySearch(
            SearchRequest.defaults()
                .withQueryEmbedding(queryEmbedding)
                .withTopK(topK)
        );
    }
}

4. RAG问答服务

集成检索和生成能力：

@Service
public class RAGQuestionAnsweringService {
    
    @Autowired
    private ChatClient chatClient;
    
    @Autowired
    private SemanticSearchService searchService;
    
    public String answerQuestion(String question) {
        // 检索相关文档
        List<Document> relevantDocs = searchService.searchRelevantDocuments(question, 5);
        
        // 构建提示上下文
        String context = buildContext(relevantDocs);
        
        // 构造提示模板
        PromptTemplate promptTemplate = new PromptTemplate("""
            基于以下上下文信息回答问题。如果上下文不足以回答问题，请如实告知。
            
            上下文：{context}
            
            问题：{question}
            
            请提供准确、详细的回答：
            """);
        
        Prompt prompt = promptTemplate.create(
            Map.of("context", context, "question", question)
        );
        
        // 调用AI生成回答
        return chatClient.call(prompt).getResult().getOutput().getContent();
    }
    
    private String buildContext(List<Document> documents) {
        return documents.stream()
            .map(Document::getContent)
            .collect(Collectors.joining("\n\n"));
    }
}

5. REST API接口

提供问答接口：

@RestController
@RequestMapping("/api/rag")
public class RAGController {
    
    @Autowired
    private RAGQuestionAnsweringService qaService;
    
    @PostMapping("/ask")
    public Mono<ResponseEntity<AnswerResponse>> askQuestion(
            @RequestBody QuestionRequest request) {
        return Mono.fromCallable(() -> {
            String answer = qaService.answerQuestion(request.getQuestion());
            return ResponseEntity.ok(new AnswerResponse(answer));
        });
    }
    
    @PostMapping("/upload")
    public Mono<ResponseEntity<String>> uploadDocument(
            @RequestParam("file") MultipartFile file) {
        return Mono.fromCallable(() -> {
            documentProcessor.processDocument(file);
            return ResponseEntity.ok("文档处理完成");
        });
    }
}

性能优化策略

1. 缓存优化

@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 CachedSearchService {
    
    @Cacheable(value = "searchResults", key = "#query")
    public List<Document> searchWithCache(String query, int topK) {
        return searchService.searchRelevantDocuments(query, topK);
    }
}

2. 异步处理

@Async
public CompletableFuture<String> processDocumentAsync(MultipartFile file) {
    return CompletableFuture.supplyAsync(() -> {
        documentProcessor.processDocument(file);
        return "处理完成";
    });
}

3. 批量操作优化

public void batchProcessDocuments(List<MultipartFile> files) {
    files.parallelStream()
        .forEach(this::processDocument);
}

监控与运维

1. 指标监控

@Configuration
public class MetricsConfig {
    
    @Bean
    public MeterRegistry meterRegistry() {
        return new PrometheusMeterRegistry(PrometheusConfig.DEFAULT);
    }
    
    @Bean
    public TimedAspect timedAspect(MeterRegistry registry) {
        return new TimedAspect(registry);
    }
}

@Service
public class MonitoringService {
    
    private final Counter questionCounter;
    private final Timer responseTimer;
    
    public MonitoringService(MeterRegistry registry) {
        questionCounter = registry.counter("rag.questions.total");
        responseTimer = registry.timer("rag.response.time");
    }
    
    @Timed(value = "rag.response.time", description = "问答响应时间")
    public String monitorQuestion(String question) {
        questionCounter.increment();
        return qaService.answerQuestion(question);
    }
}

2. 健康检查

@Component
public class VectorStoreHealthIndicator implements HealthIndicator {
    
    @Autowired
    private VectorStore vectorStore;
    
    @Override
    public Health health() {
        try {
            vectorStore.similaritySearch(SearchRequest.defaults().withTopK(1));
            return Health.up().build();
        } catch (Exception e) {
            return Health.down(e).build();
        }
    }
}

安全考虑

1. 输入验证

@Validated
public class QuestionRequest {
    
    @NotBlank
    @Size(max = 1000)
    private String question;
    
    // getters and setters
}

2. 速率限制

@Bean
public RedisRateLimiter redisRateLimiter() {
    return RedisRateLimiter.create(
        RedisRateLimiterConfig.builder()
            .limit(100)
            .duration(Duration.ofMinutes(1))
            .build()
    );
}

3. 敏感信息过滤

@Service
public class ContentFilterService {
    
    public String filterSensitiveContent(String text) {
        // 实现敏感词过滤逻辑
        return text.replaceAll("(?i)password|token|key", "***");
    }
}

部署与扩展

Docker容器化

FROM openjdk:17-jdk-slim

WORKDIR /app

COPY target/*.jar app.jar

EXPOSE 8080

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

Kubernetes部署

apiVersion: apps/v1
kind: Deployment
metadata:
  name: rag-service
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: rag-app
        image: rag-service:latest
        resources:
          limits:
            memory: "1Gi"
            cpu: "500m"
        env:
        - name: SPRING_PROFILES_ACTIVE
          value: "production"
---
apiVersion: v1
kind: Service
metadata:
  name: rag-service
spec:
  selector:
    app: rag-service
  ports:
  - port: 8080
    targetPort: 8080