用 Spring AI 构建企业级 RAG 系统：从零到生产
Building Enterprise RAG Systems with Spring AI: From Zero to Production
| iDev Team | 2026-08-17T10:00:00
Spring AI 1.0 发布后，Java 开发者终于有了一流的 AI 集成框架。本文以实战方式，手把手教你用 Spring AI 构建一个可投产的 RAG 系统。
With Spring AI 1.0 released, Java developers finally have a first-class AI integration framework. This hands-on guide walks you through building a production-ready RAG system with Spring AI.
为什么选择 Spring AIPython 生态有 LangChain、LlamaIndex，但对于大量使用 Spring Boot 的企业级项目，引入 Python 服务意味着额外的运维成本。Spring AI 让 Java 团队无需切换语言栈即可构建 AI 应用。RAG 系统架构我们要构建的系统包含以下组件：文档加载器：支持 PDF、Word、Markdown 格式文本分块器：基于语义的智能分块向量存储：使用 PgVector（PostgreSQL 扩展）检索器：混合检索（向量相似度 + BM25 关键词匹配）生成器：对接 Claude API 生成最终回答关键代码@Service public class RagService { private final VectorStore vectorStore; private final ChatClient chatClient; public String query(String question) { var docs = vectorStore.similaritySearch( SearchRequest.query(question).withTopK(5)); var context = docs.stream() .map(Document::getContent) .collect(Collectors.joining("\n")); return chatClient.prompt() .system("基于以下上下文回答问题：\n" + context) .user(question) .call().content(); } }性能优化使用 Redis 缓存高频查询的向量搜索结果文档分块时保留上下文重叠，提高检索准确度实施查询改写（Query Rewriting）提升检索召回率
Why Spring AIThe Python ecosystem has LangChain and LlamaIndex, but for enterprise projects heavily using Spring Boot, introducing Python services means additional operational overhead. Spring AI allows Java teams to build AI applications without switching language stacks.RAG System ArchitectureThe system we'll build includes these components:Document Loader: Supports PDF, Word, and Markdown formatsText Chunker: Semantic-based intelligent chunkingVector Store: Using PgVector (PostgreSQL extension)Retriever: Hybrid retrieval (vector similarity + BM25 keyword matching)Generator: Integrating Claude API for final answer generationKey Code@Service public class RagService { private final VectorStore vectorStore; private final ChatClient chatClient; public String query(String question) { var docs = vectorStore.similaritySearch( SearchRequest.query(question).withTopK(5)); var context = docs.stream() .map(Document::getContent) .collect(Collectors.joining("\n")); return chatClient.prompt() .system("Answer based on context:\n" + context) .user(question) .call().content(); } }Performance OptimizationUse Redis to cache vector search results for frequent queriesMaintain context overlap during document chunking to improve retrieval accuracyImplement Query Rewriting to boost retrieval recall rate