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（检索增强生成）技术，为企业提供了一种全新的智能文档问答解决方案。本文将深入探讨如何利用Spring AI框架构建高效的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扩展框架，提供了统一的API来集成各种AI模型和服务。其主要特性包括：

模型抽象层：统一访问不同AI提供商的模型
提示工程支持：提供灵活的提示模板和变量填充机制
工具调用框架：支持AI模型调用外部工具和服务
会话内存管理：维护对话上下文和历史记录

RAG技术原理

RAG（Retrieval-Augmented Generation）是一种结合检索和生成的AI技术架构：

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、可靠的回答

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     |
|  - API接口        |     |  - RAG服务      |     |  - 文档存储       |
+-------------------+     +-----------------+     +-------------------+
                              |
                              v
                        +-------------------+
                        |  外部服务层       |
                        |  - Embedding模型  |
                        |  - LLM模型        |
                        +-------------------+

核心组件

1. 文档处理管道

@Component
public class DocumentProcessingPipeline {
    
    @Autowired
    private DocumentLoader documentLoader;
    
    @Autowired
    private TextSplitter textSplitter;
    
    @Autowired
    private EmbeddingClient embeddingClient;
    
    @Autowired
    private VectorStore vectorStore;
    
    public void processDocument(String documentPath) {
        // 加载文档
        Document document = documentLoader.load(documentPath);
        
        // 文本分割
        List<TextSegment> segments = textSplitter.split(document.getContent());
        
        // 生成嵌入向量
        List<Embedding> embeddings = embeddingClient.embed(segments);
        
        // 存储到向量数据库
        vectorStore.add(segments, embeddings);
    }
}

2. RAG检索服务

@Service
public class RagRetrievalService {
    
    @Autowired
    private VectorStore vectorStore;
    
    @Autowired
    private EmbeddingClient embeddingClient;
    
    public List<TextSegment> retrieveRelevantContent(String query, int topK) {
        // 生成查询向量
        Embedding queryEmbedding = embeddingClient.embed(query);
        
        // 相似度检索
        return vectorStore.similaritySearch(queryEmbedding, topK);
    }
}

3. AI生成服务

@Service
public class AIGenerationService {
    
    @Autowired
    private ChatClient chatClient;
    
    public String generateAnswer(String question, List<TextSegment> context) {
        // 构建提示模板
        String promptTemplate = """
        基于以下上下文信息回答问题：
        
        {context}
        
        问题：{question}
        
        请提供准确、简洁的回答。如果上下文信息不足以回答问题，请明确说明。
        """;
        
        // 填充提示
        String contextStr = context.stream()
                .map(TextSegment::getText)
                .collect(Collectors.joining("\n\n"));
        
        Prompt prompt = new Prompt(promptTemplate
                .replace("{context}", contextStr)
                .replace("{question}", question));
        
        // 调用AI模型生成回答
        ChatResponse response = chatClient.call(prompt);
        
        return response.getResult().getOutput().getContent();
    }
}

关键技术实现

向量数据库集成

Redis向量存储配置

@Configuration
public class VectorStoreConfig {
    
    @Bean
    public VectorStore vectorStore(RedisConnectionFactory connectionFactory,
                                  EmbeddingClient embeddingClient) {
        return new RedisVectorStore(
            RedisVectorStoreOptions.builder()
                .connectionFactory(connectionFactory)
                .indexName("document_vectors")
                .embeddingClient(embeddingClient)
                .build()
        );
    }
}

Milvus向量存储配置

@Configuration
public class MilvusConfig {
    
    @Value("${milvus.host}")
    private String host;
    
    @Value("${milvus.port}")
    private int port;
    
    @Bean
    public MilvusServiceClient milvusClient() {
        return new MilvusServiceClient(
            ConnectParam.newBuilder()
                .withHost(host)
                .withPort(port)
                .build()
        );
    }
}

Embedding模型集成

OpenAI Embedding配置

@Configuration
public class EmbeddingConfig {
    
    @Bean
    @ConditionalOnProperty(name = "ai.embedding.provider", havingValue = "openai")
    public EmbeddingClient openAiEmbeddingClient(
            @Value("${openai.api-key}") String apiKey) {
        OpenAiEmbeddingOptions options = OpenAiEmbeddingOptions.builder()
                .apiKey(apiKey)
                .model("text-embedding-ada-002")
                .build();
        
        return new OpenAiEmbeddingClient(options);
    }
}

本地Ollama Embedding配置

@Configuration
public class OllamaConfig {
    
    @Bean
    @ConditionalOnProperty(name = "ai.embedding.provider", havingValue = "ollama")
    public EmbeddingClient ollamaEmbeddingClient(
            @Value("${ollama.base-url}") String baseUrl) {
        OllamaEmbeddingOptions options = OllamaEmbeddingOptions.builder()
                .baseUrl(baseUrl)
                .model("nomic-embed-text")
                .build();
        
        return new OllamaEmbeddingClient(options);
    }
}

文档加载与处理

多格式文档加载器

@Component
public class MultiFormatDocumentLoader {
    
    private final Map<String, DocumentLoader> loaders = new HashMap<>();
    
    public MultiFormatDocumentLoader() {
        loaders.put("pdf", new PdfDocumentLoader());
        loaders.put("docx", new DocxDocumentLoader());
        loaders.put("txt", new TextDocumentLoader());
        loaders.put("html", new HtmlDocumentLoader());
    }
    
    public Document load(String filePath) throws IOException {
        String extension = getFileExtension(filePath);
        DocumentLoader loader = loaders.get(extension.toLowerCase());
        
        if (loader == null) {
            throw new IllegalArgumentException("Unsupported file format: " + extension);
        }
        
        return loader.load(new File(filePath));
    }
    
    private String getFileExtension(String filename) {
        return FilenameUtils.getExtension(filename);
    }
}

智能文本分割器

@Component
public class SmartTextSplitter implements TextSplitter {
    
    @Value("${text.splitter.chunk-size:1000}")
    private int chunkSize;
    
    @Value("${text.splitter.overlap:200}")
    private int overlap;
    
    @Override
    public List<TextSegment> split(String text) {
        List<TextSegment> segments = new ArrayList<>();
        
        // 首先按段落分割
        String[] paragraphs = text.split("\\n\\n");
        
        for (String paragraph : paragraphs) {
            if (paragraph.length() <= chunkSize) {
                segments.add(new TextSegment(paragraph));
            } else {
                // 长段落需要进一步分割
                segments.addAll(splitLongText(paragraph));
            }
        }
        
        return segments;
    }
    
    private List<TextSegment> splitLongText(String text) {
        List<TextSegment> chunks = new ArrayList<>();
        int start = 0;
        
        while (start < text.length()) {
            int end = Math.min(start + chunkSize, text.length());
            
            // 尝试在句子边界处分割
            if (end < text.length()) {
                int sentenceEnd = findSentenceEnd(text, end);
                if (sentenceEnd > start) {
                    end = sentenceEnd;
                }
            }
            
            String chunk = text.substring(start, end);
            chunks.add(new TextSegment(chunk));
            
            start = end - overlap;
            if (start < 0) start = 0;
        }
        
        return chunks;
    }
    
    private int findSentenceEnd(String text, int position) {
        // 查找最近的句子结束标点
        for (int i = position; i < text.length(); i++) {
            char c = text.charAt(i);
            if (c == '.' || c == '!' || c == '?' || c == '。' || c == '！' || c == '？') {
                return i + 1;
            }
        }
        return position;
    }
}

性能优化策略

缓存机制

@Service
public class QueryCacheService {
    
    @Autowired
    private CacheManager cacheManager;
    
    @Cacheable(value = "ragResponses", key = "#question.hashCode()")
    public String getCachedResponse(String question, List<TextSegment> context) {
        // 如果缓存命中，直接返回null，触发重新生成
        return null;
    }
    
    public void cacheResponse(String question, String response) {
        Cache cache = cacheManager.getCache("ragResponses");
        if (cache != null) {
            cache.put(question.hashCode(), response);
        }
    }
}

批量处理优化

@Service
public class BatchProcessingService {
    
    @Async
    public CompletableFuture<Void> processDocumentsInBatch(List<String> documentPaths) {
        return CompletableFuture.runAsync(() -> {
            documentPaths.parallelStream()
                .forEach(this::processSingleDocument);
        });
    }
    
    private void processSingleDocument(String documentPath) {
        try {
            documentProcessingPipeline.processDocument(documentPath);
        } catch (Exception e) {
            log.error("Failed to process document: " + documentPath, e);
        }
    }
}

安全与监控

访问控制

@Configuration
@EnableWebSecurity
public class SecurityConfig extends WebSecurityConfigurerAdapter {
    
    @Override
    protected void configure(HttpSecurity http) throws Exception {
        http.authorizeRequests()
            .antMatchers("/api/documents/upload").hasRole("ADMIN")
            .antMatchers("/api/query").authenticated()
            .anyRequest().permitAll()
            .and()
            .oauth2ResourceServer().jwt();
    }
}

监控指标

@Component
public class MetricsService {
    
    private final MeterRegistry meterRegistry;
    
    public MetricsService(MeterRegistry meterRegistry) {
        this.meterRegistry = meterRegistry;
    }
    
    public void recordQueryLatency(long latencyMs, boolean success) {
        Timer.builder("rag.query.latency")
            .tag("success", String.valueOf(success))
            .register(meterRegistry)
            .record(latencyMs, TimeUnit.MILLISECONDS);
    }
    
    public void recordCacheHit(boolean hit) {
        Counter.builder("rag.cache.hits")
            .tag("hit", String.valueOf(hit))
            .register(meterRegistry)
            .increment();
    }
}

部署与运维

Docker容器化

FROM openjdk:17-jdk-slim

WORKDIR /app

COPY target/rag-system.jar app.jar
COPY application.properties application.properties

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
        env:
        - name: SPRING_PROFILES_ACTIVE
          value: "prod"
        resources:
          requests:
            memory: "2Gi"
            cpu: "1000m"
          limits:
            memory: "4Gi"
            cpu: "2000m"
---
apiVersion: v1
kind: Service
metadata:
  name: rag-service
spec:
  selector:
    app: rag-system
  ports:
  - port: 80
    targetPort: 8080