SAP HANA 云
先决条件
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您需要一个 SAP HANA 云向量引擎帐户 - 请参考 SAP HANA 云向量引擎 - 申请试用帐户 指南创建试用帐户。
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如果需要,则需要一个用于 EmbeddingModel 的 API 密钥,以生成向量存储保存的嵌入。
自动配置
Spring AI 为 SAP Hana 向量存储提供 Spring Boot 自动配置。要启用它,请将以下依赖项添加到您项目的 Maven pom.xml
文件
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-hanadb-store-spring-boot-starter</artifactId>
</dependency>
或您的 Gradle build.gradle
构建文件。
dependencies {
implementation 'org.springframework.ai:spring-ai-hanadb-store-spring-boot-starter'
}
请参考 依赖管理 部分将 Spring AI BOM 添加到您的构建文件。 |
请查看 配置参数 列表,了解向量存储的默认值和配置选项。
请参考 仓库 部分将里程碑和/或快照仓库添加到您的构建文件。 |
此外,您还需要一个已配置的 EmbeddingModel
bean。有关更多信息,请参考 EmbeddingModel 部分。
HanaCloudVectorStore 属性
您可以在 Spring Boot 配置中使用以下属性来自定义 SAP Hana 向量存储。它使用 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。
属性 | 描述 | 默认值 |
---|---|---|
|
驱动类名称 |
com.sap.db.jdbc.Driver |
|
Hana 数据源 URL |
- |
|
Hana 数据源用户名 |
- |
|
Hana 数据源密码 |
- |
|
待办事项 |
- |
|
待办事项 |
- |
|
是否初始化所需的模式 |
|
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
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在 SAP Hana DB 中创建一个名为
CRICKET_WORLD_CUP
的表
CREATE TABLE CRICKET_WORLD_CUP ( _ID VARCHAR2(255) PRIMARY KEY, CONTENT CLOB, EMBEDDING REAL_VECTOR(1536) )
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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>${spring-ai-version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-pdf-document-reader</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-hanadb-store-spring-boot-starter</artifactId>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>1.18.30</version>
<scope>provided</scope>
</dependency>
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在
application.properties
文件中添加以下属性
spring.ai.openai.api-key=${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 Entity
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.HanaVectorEntity;
@Entity
@Table(name = "CRICKET_WORLD_CUP")
@Data
@Jacksonized
@NoArgsConstructor
public class CricketWorldCup extends HanaVectorEntity {
@Column(name = "content")
private String content;
}
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创建一个名为
CricketWorldCupRepository
的Repository
,它实现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.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);
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 entityManager.createNativeQuery(sql)
.setParameter("ids", idList)
.executeUpdate();
}
@Override
@Transactional
public int deleteAllEmbeddings(String tableName) {
String sql = String.format("""
DELETE FROM %s
""", tableName);
return 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 entityManager.createNativeQuery(sql, CricketWorldCup.class)
.setParameter("topK", topK)
.setParameter("queryEmbedding", queryEmbedding)
.getResultList();
}
}
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现在,创建一个 REST 控制器类
CricketWorldCupHanaController
,并将ChatModel
和VectorStore
作为依赖项自动装配。在这个控制器类中,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,以便其数据作为嵌入存储在 SAP Hana Cloud 向量数据库中 -
/ai/hana-vector-store/cricket-world-cup
- 用于使用 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.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());
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::getContent).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 = chatModel.call(prompt).getResult().getOutput().getContent();
log.info("Generation: {}", generation);
return Map.of("generation", generation);
}
}
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使用来自维基百科的
上下文
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。