Spring AI
The Spring AI 项目旨在成为 Java 世界中的类似 LangChain 的工具。
将 Spring AI 与 Spring Data Neo4j 结合，可基于现有领域模型构建图结构，并使用向量嵌入进行丰富。
安装
如果您使用的是 Spring Initializr，可以将您选择的 Spring AI 模型以及 Neo4j Vector Database 添加为项目依赖，系统会自动拉取所有相关组件。
若手动添加依赖，Spring AI 尚未正式发布为通用库，您需要在 pom.xml 中同时添加依赖以及 snapshot 与 milestone 仓库，具体请参阅 Getting Started 文档 中的说明。
接下来需要添加 Neo4j Vector Database 的配置，同时配合自定义驱动和嵌入客户端的配置。最简便的做法是在主应用类中为这些组件分别创建 Spring Bean。文档中提到了这一点 documentation，但未提供完整代码。下面示例展示了如何配置 Neo4j Vector Database（请根据您的实际配置修改 vectorStore Bean）。
return new OpenAiEmbeddingModel(new OpenAiApi(System.getenv("SPRING_AI_OPENAI_API_KEY")));
}
@Bean
public Neo4jVectorStore vectorStore(Driver driver, EmbeddingModel embeddingModel) {
return new Neo4jVectorStore(driver, embeddingModel,
Neo4jVectorStore.Neo4jVectorStoreConfig.builder()
.withIndexName("form_10k_chunks")
.withLabel("Chunk")
.withEmbeddingProperty("textEmbedding")
.build(), true);
}
}现在我们已经完成向量存储的配置，可以通过三步流程使用 Spring AI 进行检索增强生成（RAG）。
-
调用向量相似度搜索方法，检索最相似的文档。
-
将相似文档作为输入，执行检索查询，从图中拉取相关实体。
-
将相似文档（以及它们关联的图实体）作为提示的输入，交给 LLM 生成响应。
下面的代码片段演示了如何使用 Spring AI 与 Neo4j Vector Store 实现 RAG。
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Neo4j Vector 集成文档可在 Spring AI Reference Guide 中查阅。
入门套件
在任何新技术领域的入门都可能让人望而却步。Neo4j 正在推出一些面向 GenAI 与 Neo4j 的预包装解决方案，旨在通过提供包含关键技术（包括 Spring AI）的入门套件项目，让过程更简单。您可以在 Github 上找到 Spring AI 入门套件代码，以及一篇[包含更多信息的博客文章]。