Spring AI
该 Spring AI 项目旨在成为 Java 世界中的 LangChain 之类的工具。
将 Spring AI 与 Spring Data Neo4j 结合使用，可以基于现有的领域模型构建，将其转换为图形，并使用向量嵌入对其进行丰富。
安装
如果您正在使用 Spring Initializr，您可以将您选择的 Spring AI 模型和 Neo4j 向量数据库 作为依赖项添加到您的项目中，它将引入所有相关的部分。
如果您手动添加依赖项，Spring AI 尚未成为通用发布库，因此您需要添加依赖项以及快照和里程碑存储库到您的 pom.xml 文件中，如 入门文档 中所述。
接下来，您需要添加 Neo4j 向量数据库的配置，这也需要伴随着自定义驱动程序和嵌入客户端配置。可能最简单的方法是在主应用程序类中为每个组件创建 Spring Bean。这在 文档 中有提到，但没有提供完整的代码。下面显示了如何配置 Neo4j 向量数据库的示例（请注意，您需要更改 vectorStore bean 以匹配您的特定配置）
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);
}
}现在我们已经配置了向量存储，我们可以通过三步流程使用 Spring AI 进行检索增强生成 (RAG)。
-
调用向量相似性搜索方法以检索最相似的文档。
-
使用相似文档作为检索查询的输入，以从图中提取相关实体。
-
将相似文档（及其相关图实体）作为提示的输入，LLM 将使用该提示生成响应。
下面的代码片段演示了如何使用 Spring AI 与 Neo4j 向量存储进行 RAG
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Neo4j 向量集成文档可在 Spring AI 参考指南 中找到。
入门套件
在任何新的技术领域入门都可能让人感到畏惧。Neo4j 一直致力于为 GenAI 和 Neo4j 提供一些预打包的解决方案，希望通过提供包含一些关键技术的入门套件项目来简化流程，其中包括 Spring AI。您可以在 Github 上找到 Spring AI 入门套件代码，以及一篇 [包含更多信息的博文。