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往往效率低下，难以满足快速获取准确信息的需求。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。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：将检索到的相关信息与用户问题结合
生成阶段：基于增强的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

核心组件

1. 文档处理模块

@Component
public class DocumentProcessor {
    
    @Autowired
    private EmbeddingModel embeddingModel;
    
    public List<DocumentChunk> processDocument(MultipartFile file) {
        // 文档解析、分块、向量化
        List<String> chunks = splitDocument(file);
        List<Embedding> embeddings = embeddingModel.embed(chunks);
        return createDocumentChunks(chunks, embeddings);
    }
    
    private List<String> splitDocument(MultipartFile file) {
        // 实现文档分块逻辑
        return Arrays.asList("chunk1", "chunk2", "chunk3");
    }
}

2. 向量检索模块

@Service
public class VectorSearchService {
    
    @Autowired
    private VectorStore vectorStore;
    
    @Autowired
    private EmbeddingModel embeddingModel;
    
    public List<SearchResult> searchSimilarDocuments(String query, int topK) {
        Embedding queryEmbedding = embeddingModel.embed(query);
        return vectorStore.similaritySearch(queryEmbedding, topK);
    }
}

3. AI生成模块

@Service
public class AIGenerationService {
    
    @Autowired
    private ChatClient chatClient;
    
    public String generateAnswer(String question, List<SearchResult> context) {
        String prompt = buildRAGPrompt(question, context);
        return chatClient.call(prompt);
    }
    
    private String buildRAGPrompt(String question, List<SearchResult> context) {
        StringBuilder promptBuilder = new StringBuilder();
        promptBuilder.append("基于以下上下文信息，请回答用户的问题：\n\n");
        
        for (SearchResult result : context) {
            promptBuilder.append("上下文：").append(result.getContent()).append("\n\n");
        }
        
        promptBuilder.append("问题：").append(question).append("\n\n");
        promptBuilder.append("回答：");
        
        return promptBuilder.toString();
    }
}

实战开发步骤

1. 环境准备

Maven依赖配置

<dependencies>
    <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>
    
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
</dependencies>

2. 应用配置

application.yml配置

spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
      chat:
        options:
          model: gpt-3.5-turbo
          temperature: 0.7
    
    vectorstore:
      pgvector:
        enabled: true
        index-type: HNSW
        dimensions: 1536

  datasource:
    url: jdbc:postgresql://localhost:5432/vector_db
    username: postgres
    password: password

3. 核心业务实现

文档上传接口

@RestController
@RequestMapping("/api/documents")
public class DocumentController {
    
    @Autowired
    private DocumentService documentService;
    
    @PostMapping("/upload")
    public ResponseEntity<String> uploadDocument(
            @RequestParam("file") MultipartFile file,
            @RequestParam("category") String category) {
        try {
            documentService.processAndStoreDocument(file, category);
            return ResponseEntity.ok("文档上传成功");
        } catch (Exception e) {
            return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR)
                    .body("文档处理失败: " + e.getMessage());
        }
    }
}

问答接口

@RestController
@RequestMapping("/api/qa")
public class QAController {
    
    @Autowired
    private QAService qaService;
    
    @PostMapping("/ask")
    public ResponseEntity<AnswerResponse> askQuestion(
            @RequestBody QuestionRequest request) {
        try {
            AnswerResponse response = qaService.answerQuestion(request.getQuestion());
            return ResponseEntity.ok(response);
        } catch (Exception e) {
            return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR)
                    .body(new AnswerResponse("系统繁忙，请稍后重试"));
        }
    }
}

4. 高级特性实现

对话记忆管理

@Component
public class ConversationMemoryManager {
    
    private final Map<String, List<ChatMessage>> conversationMemories = new ConcurrentHashMap<>();
    
    public void addMessage(String sessionId, ChatMessage message) {
        conversationMemories
            .computeIfAbsent(sessionId, k -> new ArrayList<>())
            .add(message);
        
        // 限制对话历史长度，防止token超限
        if (conversationMemories.get(sessionId).size() > 10) {
            conversationMemories.get(sessionId).remove(0);
        }
    }
    
    public List<ChatMessage> getConversationHistory(String sessionId) {
        return conversationMemories.getOrDefault(sessionId, new ArrayList<>());
    }
}

智能代理（Agent）实现

@Service
public class DocumentAgent {
    
    @Autowired
    private VectorSearchService searchService;
    
    @Autowired
    private AIGenerationService generationService;
    
    @Autowired
    private ToolExecutionFramework toolFramework;
    
    public String processQuery(String query, String sessionId) {
        // 1. 检索相关文档
        List<SearchResult> relevantDocs = searchService.searchSimilarDocuments(query, 5);
        
        // 2. 判断是否需要工具调用
        if (requiresToolExecution(query)) {
            return toolFramework.executeTool(query, relevantDocs);
        }
        
        // 3. 生成回答
        return generationService.generateAnswer(query, relevantDocs);
    }
    
    private boolean requiresToolExecution(String query) {
        // 实现工具调用判断逻辑
        return query.contains("计算") || query.contains("查询") || query.contains("获取");
    }
}

性能优化策略

1. 向量索引优化

-- 创建高效的HNSW索引
CREATE INDEX ON documents 
USING hnsw (embedding vector_cosine_ops) 
WITH (m = 16, ef_construction = 200);

2. 缓存策略

@Configuration
@EnableCaching
public class CacheConfig {
    
    @Bean
    public CacheManager cacheManager() {
        CaffeineCacheManager cacheManager = new CaffeineCacheManager();
        cacheManager.setCaffeine(Caffeine.newBuilder()
            .expireAfterWrite(30, TimeUnit.MINUTES)
            .maximumSize(1000));
        return cacheManager;
    }
}

@Service
public class CachedSearchService {
    
    @Autowired
    private VectorSearchService searchService;
    
    @Cacheable(value = "searchResults", key = "#query")
    public List<SearchResult> searchWithCache(String query, int topK) {
        return searchService.searchSimilarDocuments(query, topK);
    }
}

3. 批量处理优化

@Async
public CompletableFuture<Void> batchProcessDocuments(List<MultipartFile> files) {
    return CompletableFuture.runAsync(() -> {
        files.parallelStream().forEach(file -> {
            try {
                processAndStoreDocument(file, "batch");
            } catch (Exception e) {
                log.error("文档处理失败: {}", file.getOriginalFilename(), e);
            }
        });
    });
}

错误处理与监控

1. 统一异常处理

@ControllerAdvice
public class GlobalExceptionHandler {
    
    @ExceptionHandler(AIException.class)
    public ResponseEntity<ErrorResponse> handleAIException(AIException ex) {
        ErrorResponse error = new ErrorResponse("AI服务异常", ex.getMessage());
        return ResponseEntity.status(HttpStatus.SERVICE_UNAVAILABLE).body(error);
    }
    
    @ExceptionHandler(VectorStoreException.class)
    public ResponseEntity<ErrorResponse> handleVectorStoreException(VectorStoreException ex) {
        ErrorResponse error = new ErrorResponse("向量存储异常", ex.getMessage());
        return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).body(error);
    }
}

2. 监控指标

@Component
public class SystemMetrics {
    
    private final MeterRegistry meterRegistry;
    
    public SystemMetrics(MeterRegistry meterRegistry) {
        this.meterRegistry = meterRegistry;
    }
    
    public void recordSearchLatency(long milliseconds) {
        meterRegistry.timer("rag.search.latency")
            .record(milliseconds, TimeUnit.MILLISECONDS);
    }
    
    public void recordGenerationLatency(long milliseconds) {
        meterRegistry.timer("rag.generation.latency")
            .record(milliseconds, TimeUnit.MILLISECONDS);
    }
}

部署与运维

Docker容器化部署

FROM openjdk:17-jdk-slim

WORKDIR /app

COPY target/document-qa-system.jar app.jar

EXPOSE 8080

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

Kubernetes部署配置

apiVersion: apps/v1
kind: Deployment
metadata:
  name: document-qa-system
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: app
        image: document-qa-system:latest
        resources:
          limits:
            memory: "1Gi"
            cpu: "500m"
        env:
        - name: SPRING_AI_OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: openai-secret
              key: api-key
---
apiVersion: v1
kind: Service
metadata:
  name: document-qa-service
spec:
  selector:
    app: document-qa-system
  ports:
  - port: 8080
    targetPort: 8080

最佳实践

1. 提示工程优化

public class OptimizedPromptTemplate {
    
    private static final String SYSTEM_PROMPT = """
        你是一个专业的文档问答助手。请基于提供的上下文信息回答问题。
        如果上下文信息不足以回答问题，请如实告知用户。
        回答要准确、简洁、专业。
        """;
    
    public String buildOptimizedPrompt(String question, List<SearchResult> context) {
        return String.format("""
            %s
            
            上下文信息：
            %s
            
            问题：%s
            
            请回答：
            """, SYSTEM_PROMPT, formatContext(context), question);
    }
    
    private String formatContext(List<SearchResult> context) {
        return context.stream()
            .map(SearchResult::getContent)
            .collect(Collectors.joining("\n\n"));
    }
}

2. 安全性考虑

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