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

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

# SAP HANA Cloud 配置
spring.ai.vectorstore.hana.host=your-hana-host
spring.ai.vectorstore.hana.port=your-hana-port
spring.ai.vectorstore.hana.username=your-username
spring.ai.vectorstore.hana.password=your-password
spring.ai.vectorstore.hana.table-name=MY_VECTOR_TABLE
spring.ai.vectorstore.hana.embedding-dimension=1536
spring.ai.vectorstore.hana.distance-type=COSINE_SIMILARITY
# spring.ai.vectorstore.hana.schema-name=MY_SCHEMA # 可选
# spring.ai.vectorstore.hana.ssl.enabled=true # 如果需要 SSL
# spring.ai.vectorstore.hana.ssl.trust-store-location=path/to/truststore.jks
# spring.ai.vectorstore.hana.ssl.trust-store-password=truststore_password

# 嵌入客户端配置 (例如 OpenAI)
spring.ai.openai.api-key=YOUR_OPENAI_API_KEY
spring.ai.openai.embedding.options.model=text-embedding-ada-002

import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.document.Document;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

import java.util.List;

@Service
public class MyVectorStoreService {

    private final VectorStore vectorStore; // 将自动配置 HanaVectorStore

    @Autowired
    public MyVectorStoreService(VectorStore vectorStore) {
        this.vectorStore = vectorStore;
    }

    public void addDocuments(List<Document> documents) {
        vectorStore.add(documents);
    }

    public List<Document> findSimilarDocuments(String query, int k) {
        return vectorStore.similaritySearch(query, k);
    }
}

import com.zaxxer.hikari.HikariDataSource;
import org.springframework.ai.embedding.EmbeddingClient;
import org.springframework.ai.vectorstore.HanaVectorStore;
import org.springframework.ai.vectorstore.HanaVectorStore.HanaVectorStoreConfig;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.jdbc.core.JdbcTemplate;

import javax.sql.DataSource;

@Configuration
public class MyHanaConfig {

    @Bean
    public DataSource hanaDataSource() {
        HikariDataSource dataSource = new HikariDataSource();
        dataSource.setJdbcUrl("jdbc:sap://your-hana-host:your-hana-port");
        dataSource.setUsername("your-username
        dataSource.setPassword("your-password");
        // dataSource.setSchema("MY_SCHEMA"); // 可选
        // SSL 属性 (如果需要)
        // dataSource.getDataSourceProperties().setProperty("encrypt", "true");
        // dataSource.getDataSourceProperties().setProperty("trustStore", "path/to/truststore.jks");
        // dataSource.getDataSourceProperties().setProperty("trustStorePassword", "truststore_password");
        return dataSource;
    }

    @Bean
    public JdbcTemplate jdbcTemplate(DataSource hanaDataSource) {
        return new JdbcTemplate(hanaDataSource);
    }

    @Bean
    public HanaVectorStore hanaVectorStore(JdbcTemplate jdbcTemplate, EmbeddingClient embeddingClient) {
        HanaVectorStoreConfig config = HanaVectorStoreConfig.builder()
            .tableName("MY_CUSTOM_TABLE")
            .embeddingDimension(1536)
            .distanceType(HanaVectorStoreConfig.DistanceType.COSINE_SIMILARITY)
            // .schemaName("MY_SCHEMA") // 可选
            .build();
        return new HanaVectorStore(jdbcTemplate, embeddingClient, config);
    }
}

import org.springframework.ai.vectorstore.SearchRequest;
import org.springframework.ai.vectorstore.filter.Filter;
import org.springframework.ai.vectorstore.filter.FilterExpressionBuilder;

// ...

FilterExpressionBuilder b = new FilterExpressionBuilder();

// "metadata"->>'genre' = 'drama' AND "metadata"->>'year' > 2020
// 注意：在 SAP HANA 中，JSON 字段访问通常使用 JSON 函数或路径表达式。
// 此处的示例假定元数据存储为 JSON，并且数据库配置为支持此类查询。
// 精确的 SQL 语法可能因您的 HANA 版本和表结构而异。
Filter filter = new Filter(b.eq("genre", "drama").and(b.gt("year", 2020)).build());

SearchRequest request = SearchRequest.query("A dramatic film released after 2020") // 示例查询通常保留英文
    .withTopK(5)
    .withFilter(filter);

List<Document> results = vectorStore.similaritySearch(request);

boolean deleted = vectorStore.delete(List.of("documentId1", "documentId2"));