Milvus 2.0 is a cloud-native vector database, featuring a design architecture that separates storage from computation. All components of this revamped version are stateless, greatly enhancing system resilience and flexibility. For more details about the system architecture, refer to Milvus System Architecture.

Milvus is released under the Apache 2.0 License, it was officially open-sourced in October 2019 and now is a graduate project of LF AI & Data Foundation.

A Java-style Milvus Operation Library

This component is a Milvus component written in the style of mybatis-plus. It allows you to operate Milvus in java just like using mysql, executing precise query operations, or using vectors to execute similarity queries.

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I. How to Import

<dependency>
    <groupId>plus.jdk</groupId>
    <artifactId>spring-boot-starter-milvus</artifactId>
    <version>${last.version}</version>
</dependency>

II. Milvus Configuration

plus.jdk.milvus.enabled=true
plus.jdk.milvus.host=*
plus.jdk.milvus.port=19530
plus.jdk.milvus.user-name=root
plus.jdk.milvus.*=123456

III. Define ORM Objects

import io.milvus.grpc.DataType;
import lombok.Data;
import lombok.EqualsAndHashCode;
import plus.jdk.milvus.annotation.VectorCollectionColumn;
import plus.jdk.milvus.annotation.VectorCollectionName;
import plus.jdk.milvus.record.VectorModel;

import java.util.List;

@Data
@EqualsAndHashCode(callSuper = true)
@VectorCollectionName(name = "user_blog", description = "User blog vector table")
public class UserBlogVector extends VectorModel<UserBlogVector> {

    /**
     * Primary Key
     */
    @VectorCollectionColumn(name = "id", dataType = DataType.Int64, primary = true)
    private Long id;

    /**
     * uid
     */
    @VectorCollectionColumn(name = "uid", dataType = DataType.Int64)
    private Long uid;

    /**
     * Blog text
     */
    @VectorCollectionColumn(name = "blog_text", dataType = DataType.VarChar, maxLength = 1024)
    private String blogText;

    /**
     * Blog type
     */
    @VectorCollectionColumn(name = "blog_type", dataType = DataType.JSON)
    private JSONObject blogType;

    /**
     * Blog text vector, the blog text vector used here is m3e embedding, so it is 768
     */
    @VectorCollectionColumn(name = "v_blog_text", dataType = DataType.FloatVector, vectorDimension = 768)
    private List<Float> blogTextVector;
}

IV. Define DAO Layer

We have encapsulated many basic operation APIs for milvus in VectorModelRepositoryImpl.

import com.weibo.biz.omniscience.dolly.milvus.entity.UserBlogVector;
import plus.jdk.milvus.annotation.VectorRepository;
import plus.jdk.milvus.record.VectorModelRepositoryImpl;

@VectorRepository
public class UserBlogVectorDao extends VectorModelRepositoryImpl<UserBlogVector> {
}

Some commonly used API examples are as follows:

import com.alibaba.fastjson.JSONObject;
import lombok.extern.slf4j.Slf4j;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import plus.jdk.milvus.common.MilvusException;
import plus.jdk.milvus.common.chat.ChatClient;
import plus.jdk.milvus.dao.UserBlogVectorDao;
import plus.jdk.milvus.model.HNSWIIndexExtra;

import java.util.Arrays;
import java.util.Collections;
import java.util.List;


@Slf4j
@SpringBootTest
public class UserBlogVectorServiceTest {

    @Autowired
    private UserBlogVectorDao userBlogVectorDao;

    @Autowired
    private ChatClient chatClient;

    /**
     * Create collection and index
     */
    @Test
    public void createCollection() throws MilvusException {
        boolean ret = userBlogVectorDao.createCollection();
        HNSWIIndexExtra extra = new HNSWIIndexExtra();
        extra.setM(16);
        extra.setEfConstruction(8);
        userBlogVectorDao.createIndex("idx_blog_vector",
                UserBlogVector::getBlogTextVector, extra);
        userBlogVectorDao.loadCollection();
    }

    /**
     * Insert record into collection
     */
    @Test
    public void insertVector() throws MilvusException {
        String text = "Hi guys!! Just out of the oven nine pictures. Vote! Like figure few";
        Long uid = 2656274875L;
        UserBlogVector userBlogVector = new UserBlogVector();
        userBlogVector.setBlogText(text);
        userBlogVector.setUid(uid);
        userBlogVector.setBlogType(new JSONObject() {{
            put("type", Arrays.asList("1", "2"));
        }});
        List<List<Float>> embedding = chatClient.getEmbedding(Collections.singletonList(text));
        userBlogVector.setBlogTextVector(embedding.get(0));
        boolean ret = userBlogVectorDao.insert(userBlogVector);
        log.info("{}", ret);
    }

    /**
     * Use other fields to lookup related content
     */
    @Test
    public void query() throws MilvusException {
        LambdaQueryWrapper<UserBlogVector> wrapper = new LambdaQueryWrapper<>();
        wrapper.eq(UserBlogVector::getUid, 2656274875L)
                .or()
                .ne(UserBlogVector::getUid, 1234567890L)
                .or(jsonWrapper ->
                        jsonWrapper
                                .jsonContains(UserBlogVector::getBlogType, 1, "type")
                                .jsonContainsAll(UserBlogVector::getBlogType, Arrays.asList("1", "2"), "type")
                                .or()
                                .jsonContainsAny(UserBlogVector::getBlogType, Arrays.asList("112", "312"), "tasd")
                );
        List<UserBlogVector> queryResults = userBlogVectorDao.query(wrapper);
        log.info("{}", queryResults);
    }

    /**
     * Use a vector to find the most similar content. You can also combine it with other fields for query filtering
     */
    @Test
    public void search() throws MilvusException {
        String text = "Hi guys!! Just out of the oven nine pictures. Vote! Like figure few";
        LambdaSearchWrapper<UserBlogVector> wrapper = new LambdaSearchWrapper<>();
        List<List<Float>> embedding = chatClient.getEmbedding(Collections.singletonList(text));
        wrapper.vector(UserBlogVector::getBlogTextVector, embedding.get(0));
        wrapper.setTopK(10);
        wrapper.eq(UserBlogVector::getUid, 2656274875L);
        wrapper.jsonContainsAny(UserBlogVector::getBlogType, Arrays.asList("1", "2"), "type");
        List<UserBlogVector> searchResults = userBlogVectorDao.search(wrapper);
        log.info("{}", searchResults);
    }

    /**
     * Use primary key to delete record
     */
    @Test
    public void deleteRecord() throws MilvusException {
        boolean ret = userBlogVectorDao.remove(12345556);
        log.info("{}", ret);
    }
}