SAP HANA

SAP HANA Cloud

先决条件

• 您需要一个 SAP HANA Cloud 向量引擎账户 —— 请参考 SAP HANA Cloud 向量引擎 - 创建试用账户 指南来创建试用账户。
• 如需要，可获取 EmbeddingModel 的 API 密钥，用于生成向量存储所嵌入的内容。

自动配置

Spring AI 并未为 SAP Hana 向量存储提供专门的模块。用户需在应用程序中自行配置，利用 Spring AI 的标准向量存储模块 ——spring-ai-hanadb-store，来实现对 SAP Hana 向量存储的支持。

此外，您还需要配置一个 EmbeddingModel bean。更多信息请参阅 EmbeddingModel 部分。

Hana 云向量存储属性

您可以在 Spring Boot 配置中使用以下属性来定制 SAP Hana 向量存储。它利用 spring.datasource 属性来配置 Hana 数据源，并使用 spring.ai.vectorstore.hanadb 属性来配置 Hana 向量存储。

构建一个示例 RAG 应用程序

展示如何设置一个使用 SAP Hana Cloud 作为向量数据库并利用 OpenAI 实现 RAG 模式的项目

在 SAP Hana 数据库中创建 CRICKET_WORLD_CUP 表：

CREATE TABLE CRICKET_WORLD_CUP (
    _ID VARCHAR2(255) PRIMARY KEY,
    CONTENT CLOB,
    EMBEDDING REAL_VECTOR(1536)
)

在您的 pom.xml 中添加以下依赖项

您可以将属性 spring-ai-version 设置为 <spring-ai-version>1.0.0-SNAPSHOT</spring-ai-version>：

<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-bom</artifactId>
            <version>${spring-ai-version}</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

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

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-pdf-document-reader</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-openai</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-vector-store-hana</artifactId>
</dependency>

<dependency>
    <groupId>org.projectlombok</groupId>
    <artifactId>lombok</artifactId>
    <version>1.18.30</version>
    <scope>provided</scope>
</dependency>

在 application.properties 文件中添加以下属性：

spring.ai.openai.api-key=${OPENAI_API_KEY}
spring.ai.openai.embedding.options.model=text-embedding-ada-002

spring.datasource.driver-class-name=com.sap.db.jdbc.Driver
spring.datasource.url=${HANA_DATASOURCE_URL}
spring.datasource.username=${HANA_DATASOURCE_USERNAME}
spring.datasource.password=${HANA_DATASOURCE_PASSWORD}

spring.ai.vectorstore.hanadb.tableName=CRICKET_WORLD_CUP
spring.ai.vectorstore.hanadb.topK=3

创建一个名为 CricketWorldCup 的实体类，该类继承自 HanaVectorEntity：

package com.interviewpedia.spring.ai.hana;

import jakarta.persistence.Column;
import jakarta.persistence.Entity;
import jakarta.persistence.Table;
import lombok.Data;
import lombok.NoArgsConstructor;
import lombok.extern.jackson.Jacksonized;
import org.springframework.ai.vectorstore.hanadb.HanaVectorEntity;

@Entity
@Table(name = "CRICKET_WORLD_CUP")
@Data
@Jacksonized
@NoArgsConstructor
public class CricketWorldCup extends HanaVectorEntity {
    @Column(name = "content")
    private String content;
}

创建一个名为 CricketWorldCupRepository 的 Repository，实现 HanaVectorRepository 接口：

package com.interviewpedia.spring.ai.hana;

import jakarta.persistence.EntityManager;
import jakarta.persistence.PersistenceContext;
import jakarta.transaction.Transactional;
import org.springframework.ai.vectorstore.hanadb.HanaVectorRepository;
import org.springframework.stereotype.Repository;

import java.util.List;

@Repository
public class CricketWorldCupRepository implements HanaVectorRepository<CricketWorldCup> {
    @PersistenceContext
    private EntityManager entityManager;

    @Override
    @Transactional
    public void save(String tableName, String id, String embedding, String content) {
        String sql = String.format("""
                INSERT INTO %s (_ID, EMBEDDING, CONTENT)
                VALUES(:_id, TO_REAL_VECTOR(:embedding), :content)
                """, tableName);

        this.entityManager.createNativeQuery(sql)
                .setParameter("_id", id)
                .setParameter("embedding", embedding)
                .setParameter("content", content)
                .executeUpdate();
    }

    @Override
    @Transactional
    public int deleteEmbeddingsById(String tableName, List<String> idList) {
        String sql = String.format("""
                DELETE FROM %s WHERE _ID IN (:ids)
                """, tableName);

        return this.entityManager.createNativeQuery(sql)
                .setParameter("ids", idList)
                .executeUpdate();
    }

    @Override
    @Transactional
    public int deleteAllEmbeddings(String tableName) {
        String sql = String.format("""
                DELETE FROM %s
                """, tableName);

        return this.entityManager.createNativeQuery(sql).executeUpdate();
    }

    @Override
    public List<CricketWorldCup> cosineSimilaritySearch(String tableName, int topK, String queryEmbedding) {
        String sql = String.format("""
                SELECT TOP :topK * FROM %s
                ORDER BY COSINE_SIMILARITY(EMBEDDING, TO_REAL_VECTOR(:queryEmbedding)) DESC
                """, tableName);

        return this.entityManager.createNativeQuery(sql, CricketWorldCup.class)
                .setParameter("topK", topK)
                .setParameter("queryEmbedding", queryEmbedding)
                .getResultList();
    }
}

• 现在，创建一个名为 CricketWorldCupHanaController 的 REST 控制器类，并将 ChatModel 和 VectorStore 作为依赖项自动注入。在此控制器类中，创建以下 REST 端点：
  • /ai/hana-vector-store/cricket-world-cup/purge-embeddings - 用于从向量存储中清除所有嵌入
  • /ai/hana-vector-store/cricket-world-cup/upload - 用于上传 Cricket_World_Cup.pdf，使其数据作为嵌入存储在 SAP Hana 云向量数据库中
  • /ai/hana-vector-store/cricket-world-cup - 使用 SAP Hana DB 中的余弦相似度 实现 RAG

package com.interviewpedia.spring.ai.hana;

import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.chat.prompt.SystemPromptTemplate;
import org.springframework.ai.document.Document;
import org.springframework.ai.reader.pdf.PagePdfDocumentReader;
import org.springframework.ai.transformer.splitter.TokenTextSplitter;
import org.springframework.ai.vectorstore.hanadb.HanaCloudVectorStore;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.core.io.Resource;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import org.springframework.web.multipart.MultipartFile;

import java.io.IOException;
import java.util.List;
import java.util.Map;
import java.util.function.Function;
import java.util.function.Supplier;
import java.util.stream.Collectors;

@RestController
@Slf4j
public class CricketWorldCupHanaController {
    private final VectorStore hanaCloudVectorStore;
    private final ChatModel chatModel;

    @Autowired
    public CricketWorldCupHanaController(ChatModel chatModel, VectorStore hanaCloudVectorStore) {
        this.chatModel = chatModel;
        this.hanaCloudVectorStore = hanaCloudVectorStore;
    }

    @PostMapping("/ai/hana-vector-store/cricket-world-cup/purge-embeddings")
    public ResponseEntity<String> purgeEmbeddings() {
        int deleteCount = ((HanaCloudVectorStore) this.hanaCloudVectorStore).purgeEmbeddings();
        log.info("{} embeddings purged from CRICKET_WORLD_CUP table in Hana DB", deleteCount);
        return ResponseEntity.ok().body(String.format("%d embeddings purged from CRICKET_WORLD_CUP table in Hana DB", deleteCount));
    }

    @PostMapping("/ai/hana-vector-store/cricket-world-cup/upload")
    public ResponseEntity<String> handleFileUpload(@RequestParam("pdf") MultipartFile file) throws IOException {
        Resource pdf = file.getResource();
        Supplier<List<Document>> reader = new PagePdfDocumentReader(pdf);
        Function<List<Document>, List<Document>> splitter = new TokenTextSplitter();
        List<Document> documents = splitter.apply(reader.get());
        log.info("{} documents created from pdf file: {}", documents.size(), pdf.getFilename());
        this.hanaCloudVectorStore.accept(documents);
        return ResponseEntity.ok().body(String.format("%d documents created from pdf file: %s",
                documents.size(), pdf.getFilename()));
    }

    @GetMapping("/ai/hana-vector-store/cricket-world-cup")
    public Map<String, String> hanaVectorStoreSearch(@RequestParam(value = "message") String message) {
        var documents = this.hanaCloudVectorStore.similaritySearch(message);
        var inlined = documents.stream().map(Document::getText).collect(Collectors.joining(System.lineSeparator()));
        var similarDocsMessage = new SystemPromptTemplate("Based on the following: {documents}")
                .createMessage(Map.of("documents", inlined));

        var userMessage = new UserMessage(message);
        Prompt prompt = new Prompt(List.of(similarDocsMessage, userMessage));
        String generation = this.chatModel.call(prompt).getResult().getOutput().getContent();
        log.info("Generation: {}", generation);
        return Map.of("generation", generation);
    }
}

由于 HanaDB 向量存储支持未提供自动配置模块，您还需要在应用程序中提供向量存储 bean，如下例所示。

@Bean
public VectorStore hanaCloudVectorStore(CricketWorldCupRepository cricketWorldCupRepository,
        EmbeddingModel embeddingModel) {

    return HanaCloudVectorStore.builder(cricketWorldCupRepository, embeddingModel)
        .tableName("CRICKET_WORLD_CUP")
        .topK(1)
        .build();
}

使用维基百科中的上下文 PDF 文件。

前往 维基百科 并 下载 板球世界杯页面为 PDF 文件。

使用我们在上一步中创建的文件上传 REST 端点，上传此 PDF 文件。