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。传统的文档检索方式已经无法满足用户对于精准、智能问答的需求。Spring AI结合RAG（Retrieval-Augmented Generation）技术，为企业提供了一种全新的智能文档问答解决方案。本文将详细介绍如何基于Spring AI框架构建企业级智能文档问答系统。

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。它支持OpenAI、Azure OpenAI、Google Vertex AI等多种AI服务提供商。

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。其核心思想是：

首先从大量文档中检索出与问题相关的信息
然后将检索到的信息作为上下文提供给生成模型
最后生成模型基于上下文生成准确的回答

系统架构设计

整体架构

+----------------+     +----------------+     +----------------+
|  用户界面层    |     |  应用服务层    |     |  数据存储层    |
| - Web前端      |     | - Spring AI    |     | - 向量数据库   |
| - 移动端       |     | - RAG服务      |     | - 文档存储    |
+----------------+     +----------------+     +----------------+
        |                       |                       |
        | HTTP/REST             | API调用               | 数据访问
        v                       v                       v
+-------------------------------------------------------------+
|                   智能问答引擎                             |
| - 语义检索        - 上下文构建        - 回答生成           |
+-------------------------------------------------------------+

核心组件

1. 文档处理模块

@Component
public class DocumentProcessor {
    
    @Autowired
    private EmbeddingModel embeddingModel;
    
    @Autowired
    private VectorStore vectorStore;
    
    public void processDocument(String documentId, String content) {
        // 文档分块
        List<DocumentChunk> chunks = splitDocument(content);
        
        // 生成向量嵌入
        List<Embedding> embeddings = generateEmbeddings(chunks);
        
        // 存储到向量数据库
        storeToVectorDB(documentId, chunks, embeddings);
    }
    
    private List<DocumentChunk> splitDocument(String content) {
        // 实现文档分块逻辑
        return TextSplitter.split(content, 1000); // 每块1000字符
    }
}

2. 语义检索模块

@Service
public class SemanticSearchService {
    
    @Autowired
    private VectorStore vectorStore;
    
    @Autowired
    private EmbeddingModel embeddingModel;
    
    public List<SearchResult> search(String query, int topK) {
        // 生成查询向量
        Embedding queryEmbedding = embeddingModel.embed(query);
        
        // 向量相似度搜索
        return vectorStore.similaritySearch(queryEmbedding, topK);
    }
}

3. 问答生成模块

@Service
public class QAService {
    
    @Autowired
    private ChatClient chatClient;
    
    @Autowired
    private SemanticSearchService searchService;
    
    public String answerQuestion(String question) {
        // 检索相关文档
        List<SearchResult> relevantDocs = searchService.search(question, 5);
        
        // 构建提示词
        String prompt = buildPrompt(question, relevantDocs);
        
        // 生成回答
        return chatClient.generate(prompt);
    }
    
    private String buildPrompt(String question, List<SearchResult> docs) {
        StringBuilder context = new StringBuilder();
        for (SearchResult doc : docs) {
            context.append("文档内容: ").append(doc.getContent()).append("\n\n");
        }
        
        return String.format("""
            基于以下文档内容，请回答这个问题: %s
            
            %s
            
            请根据上述文档内容提供准确的回答。如果文档中没有相关信息，请说明无法回答。
            """, question, context.toString());
    }
}

向量数据库集成

Milvus向量数据库配置

spring:
  ai:
    vectorstore:
      milvus:
        host: localhost
        port: 19530
        collection-name: document_vectors
        metric-type: COSINE
        index-type: IVF_FLAT

Redis作为向量存储

@Configuration
public class VectorStoreConfig {
    
    @Bean
    public VectorStore redisVectorStore(RedisConnectionFactory connectionFactory) {
        return new RedisVectorStore(connectionFactory, "document_vectors");
    }
}

Embedding模型集成

OpenAI Embedding

spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
      embedding:
        model: text-embedding-ada-002

本地Ollama模型

spring:
  ai:
    ollama:
      base-url: http://localhost:11434
      embedding:
        model: nomic-embed-text

企业级特性实现

1. 多租户支持

@Service
public class MultiTenantQAService {
    
    @Autowired
    private TenantContext tenantContext;
    
    public String answerQuestion(String question, String tenantId) {
        tenantContext.setCurrentTenant(tenantId);
        
        // 使用租户特定的向量存储
        VectorStore tenantVectorStore = getTenantVectorStore(tenantId);
        
        // 执行检索和生成
        return generateAnswer(question, tenantVectorStore);
    }
}

2. 访问控制与审计

@Aspect
@Component
public class AuditAspect {
    
    @AfterReturning(
        pointcut = "execution(* com.example.service.QAService.answerQuestion(..))",
        returning = "result"
    )
    public void auditQuestion(JoinPoint joinPoint, Object result) {
        String question = (String) joinPoint.getArgs()[0];
        String answer = (String) result;
        
        // 记录审计日志
        auditService.logQuestion(question, answer);
    }
}

3. 性能监控

@RestController
public class QAController {
    
    @Autowired
    private Micrometer micrometer;
    
    @PostMapping("/api/ask")
    public ResponseEntity<String> askQuestion(@RequestBody QuestionRequest request) {
        Timer.Sample sample = Timer.start();
        
        try {
            String answer = qaService.answerQuestion(request.getQuestion());
            
            sample.stop(micrometer.timer("qa.response.time"));
            return ResponseEntity.ok(answer);
        } catch (Exception e) {
            micrometer.counter("qa.errors").increment();
            throw e;
        }
    }
}

部署与运维

Docker容器化部署

FROM openjdk:17-jdk-slim

WORKDIR /app

COPY target/qa-system.jar app.jar

EXPOSE 8080

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

Kubernetes部署配置

apiVersion: apps/v1
kind: Deployment
metadata:
  name: qa-system
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: qa-app
        image: qa-system:latest
        ports:
        - containerPort: 8080
        env:
        - name: SPRING_AI_OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: openai-secret
              key: api-key
---
apiVersion: v1
kind: Service
metadata:
  name: qa-service
spec:
  selector:
    app: qa-system
  ports:
  - port: 80
    targetPort: 8080

性能优化策略

1. 缓存优化

@Configuration
@EnableCaching
public class CacheConfig {
    
    @Bean
    public CacheManager cacheManager() {
        return new CaffeineCacheManager("questions", "documents");
    }
}

@Service
public class CachedQAService {
    
    @Cacheable(value = "questions", key = "#question")
    public String answerQuestion(String question) {
        // 实际的问题回答逻辑
        return qaService.answerQuestion(question);
    }
}

2. 异步处理

@Async
public CompletableFuture<String> answerQuestionAsync(String question) {
    return CompletableFuture.supplyAsync(() -> 
        qaService.answerQuestion(question)
    );
}

3. 批量处理

public Map<String, String> answerQuestions(List<String> questions) {
    return questions.parallelStream()
        .collect(Collectors.toMap(
            Function.identity(),
            this::answerQuestion
        ));
}

安全考虑

1. 输入验证

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

2. 输出过滤

public String sanitizeAnswer(String answer) {
    return HtmlUtils.htmlEscape(answer);
}

3. 速率限制

@RestController
@RateLimit(limit = 100, duration = 60) // 每分钟100次
public class QAController {
    // controller methods
}

测试策略

单元测试

@ExtendWith(MockitoExtension.class)
class QAServiceTest {
    
    @Mock
    private SemanticSearchService searchService;
    
    @Mock
    private ChatClient chatClient;
    
    @InjectMocks
    private QAService qaService;
    
    @Test
    void testAnswerQuestion() {
        // 准备测试数据
        when(searchService.search(anyString(), anyInt())
            .thenReturn(List.of(new SearchResult("相关文档内容")));
        
        when(chatClient.generate(anyString())
            .thenReturn("这是生成的回答");
        
        // 执行测试
        String result = qaService.answerQuestion("测试问题");
        
        // 验证结果
        assertEquals("这是生成的回答", result);
    }
}

集成测试

@SpringBootTest
@AutoConfigureMockMvc
class QAControllerIntegrationTest {
    
    @Autowired
    private MockMvc mockMvc;
    
    @Test
    void testAskEndpoint() throws Exception {
        mockMvc.perform(post("/api/ask")
                .contentType(MediaType.APPLICATION_JSON)
                .content("{\"question\":\"测试问题\"}"))
                .andExpect(status().isOk())
                .andExpect(jsonPath("$").isString());
    }
}

监控与日志

Prometheus监控

management:
  endpoints:
    web:
      exposure:
        include: health,info,metrics,prometheus
  metrics:
    export:
      prometheus:
        enabled: true

结构化日志

@Slf4j
@Service
public class QAService {
    
    public String answerQuestion(String question) {
        log.info("Processing question: {}", question);
        
        try {
            // 处理逻辑
            return result;
        } catch (Exception e) {
            log.error("Failed to answer question: {}", question, e);
            throw e;
        }
    }
}