milvus向量数据库

作者：青云 发布时间： 2026-05-30 阅读量：3 评论数：0
数据库
#milvus

一、官方网站

https://milvus.io/zh

二、什么是 Milvus？

Milvus是鹰科(Accipaitridae)Milvus属的一种猛禽，以飞行速度快、视力敏锐、适应性强而著称。

Zilliz 采用 Milvus 作为其开源高性能、高扩展性向量数据库的名称，该数据库可在从笔记本电脑到大规模分布式系统等各种环境中高效运行。它既是开源软件，也是云服务。

Milvus 由 Zilliz 开发，并很快捐赠给了 Linux 基金会下的 LF AI & Data 基金会，现已成为世界领先的开源向量数据库项目之一。它采用 Apache 2.0 许可发布，大多数贡献者都是高性能计算（HPC）领域的专家，擅长构建大规模系统和优化硬件感知代码。核心贡献者包括来自 Zilliz、ARM、英伟达、AMD、英特尔、Meta、IBM、Salesforce、阿里巴巴和微软的专业人士。

有趣的是，Zilliz 的每个开源项目都以鸟命名，这种命名方式象征着自由、远见和技术的敏捷发展。

简单来说，Milvus就是一款向量数据库。

三、安装 Milvus

milvus的安装有多种方式，这里使用 Docker Compose 运行 Milvus。

wget https://github.com/milvus-io/milvus/releases/download/v3.0-beta/milvus-standalone-docker-compose.yml -O docker-compose.yml

sudo docker compose up -d

Creating milvus-etcd  ... done
Creating milvus-minio ... done
Creating milvus-standalone ... done

需要注意的有三个问题：

1.文件下载后是空的

2.版本废弃了

3.权限问题

四、图形化界面

milvus的图形化界面客户端也有多种，这里将的是attu

进入https://github.com/zilliztech/attu，下载对应的安装包即可

五、操作

添加依赖

<dependency>
    <groupId>io.milvus</groupId>
    <artifactId>milvus-sdk-java</artifactId>
    <version>2.6.18</version>
</dependency>
package com.zhan.aiblog.controller;

import com.google.gson.Gson;
import com.google.gson.JsonObject;
import com.zhan.aiblog.common.api.ApiResult;
import com.zhan.aiblog.common.enums.ResultCode;
import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;
import io.milvus.v2.common.DataType;
import io.milvus.v2.common.IndexParam;
import io.milvus.v2.service.collection.request.AddFieldReq;
import io.milvus.v2.service.collection.request.CreateCollectionReq;
import io.milvus.v2.service.collection.request.GetLoadStateReq;
import io.milvus.v2.service.vector.request.InsertReq;
import io.milvus.v2.service.vector.request.SearchReq;
import io.milvus.v2.service.vector.request.data.BaseVector;
import io.milvus.v2.service.vector.request.data.FloatVec;
import io.milvus.v2.service.vector.response.InsertResp;
import io.milvus.v2.service.vector.response.SearchResp;
import io.swagger.v3.oas.annotations.Operation;
import io.swagger.v3.oas.annotations.tags.Tag;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;

import javax.naming.directory.SearchResult;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

@RestController
@RequestMapping("/api/milvus")
@Tag(name = "milvus测试接口", description = "milvus测试接口")
public class MiluvsController {

    @GetMapping("/create-collections")
    @Operation(summary = "创建 Collections", description = "创建 Collections")
    public ApiResult<ResultCode> createCollections() {
        String CLUSTER_ENDPOINT = "http://localhost:19530";
        String TOKEN = "root:Milvus";

        // 1. Connect to Milvus server
        ConnectConfig connectConfig = ConnectConfig.builder()
                .uri(CLUSTER_ENDPOINT)
                .token(TOKEN)
                .build();

        MilvusClientV2 client = new MilvusClientV2(connectConfig);

        // 3. Create a collection in customized setup mode

        // 3.1 Create schema
        CreateCollectionReq.CollectionSchema schema = client.createSchema();

        // 3.2 Add fields to schema
        schema.addField(AddFieldReq.builder()
                .fieldName("id")
                .dataType(DataType.Int64)
                .isPrimaryKey(true)
                .autoID(false)
                .build());

        schema.addField(AddFieldReq.builder()
                .fieldName("vector")
                .dataType(DataType.FloatVector)
                .dimension(5)
                .build());

        schema.addField(AddFieldReq.builder()
                .fieldName("color")
                .dataType(DataType.VarChar)
                .maxLength(512)
                .build());

        // 3.3 Prepare index parameters
        IndexParam indexParamForIdField = IndexParam.builder()
                .fieldName("id")
                .indexType(IndexParam.IndexType.AUTOINDEX)
                .build();

        IndexParam indexParamForVectorField = IndexParam.builder()
                .fieldName("vector")
                .indexType(IndexParam.IndexType.AUTOINDEX)
                .metricType(IndexParam.MetricType.COSINE)
                .build();

        List<IndexParam> indexParams = new ArrayList<>();
        indexParams.add(indexParamForIdField);
        indexParams.add(indexParamForVectorField);


        // 3.4 Create a collection with schema and index parameters
        CreateCollectionReq customizedSetupReq1 = CreateCollectionReq.builder()
                .collectionName("quick_setup")
                .collectionSchema(schema)
                .indexParams(indexParams)
                .build();

        client.createCollection(customizedSetupReq1);

        // 3.5 Get load state of the collection
        GetLoadStateReq customSetupLoadStateReq1 = GetLoadStateReq.builder()
                .collectionName("quick_setup")
                .build();

        Boolean loaded = client.getLoadState(customSetupLoadStateReq1);
        System.out.println(loaded);

        return ApiResult.success(ResultCode.SUCCESS);

    }

    @GetMapping("/insert")
    @Operation(summary = "插入数据", description = "插入数据")
    public ApiResult<ResultCode> insert() {
        MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
                .uri("http://localhost:19530")
                .token("root:Milvus")
                .build());

        Gson gson = new Gson();
        List<JsonObject> data = Arrays.asList(
                gson.fromJson("{\"id\": 0, \"vector\": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], \"color\": \"pink_8682\"}", JsonObject.class),
                gson.fromJson("{\"id\": 1, \"vector\": [0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104], \"color\": \"red_7025\"}", JsonObject.class),
                gson.fromJson("{\"id\": 2, \"vector\": [0.43742130801983836, -0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592], \"color\": \"orange_6781\"}", JsonObject.class),
                gson.fromJson("{\"id\": 3, \"vector\": [0.3172005263489739, 0.9719044792798428, -0.36981146090600725, -0.4860894583077995, 0.95791889146345], \"color\": \"pink_9298\"}", JsonObject.class),
                gson.fromJson("{\"id\": 4, \"vector\": [0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106], \"color\": \"red_4794\"}", JsonObject.class),
                gson.fromJson("{\"id\": 5, \"vector\": [0.985825131989184, -0.8144651566660419, 0.6299267002202009, 0.1206906911183383, -0.1446277761879955], \"color\": \"yellow_4222\"}", JsonObject.class),
                gson.fromJson("{\"id\": 6, \"vector\": [0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987], \"color\": \"red_9392\"}", JsonObject.class),
                gson.fromJson("{\"id\": 7, \"vector\": [-0.33445148015177995, -0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052], \"color\": \"grey_8510\"}", JsonObject.class),
                gson.fromJson("{\"id\": 8, \"vector\": [0.39524717779832685, 0.4000257286739164, -0.5890507376891594, -0.8650502298996872, -0.6140360785406336], \"color\": \"white_9381\"}", JsonObject.class),
                gson.fromJson("{\"id\": 9, \"vector\": [0.5718280481994695, 0.24070317428066512, -0.3737913482606834, -0.06726932177492717, -0.6980531615588608], \"color\": \"purple_4976\"}", JsonObject.class)
        );

        InsertReq insertReq = InsertReq.builder()
                .collectionName("quick_setup")
                .data(data)
                .build();

        InsertResp insertResp = client.insert(insertReq);
        System.out.println(insertResp);
        return ApiResult.success(ResultCode.SUCCESS);
    }


    @GetMapping("/search")
    @Operation(summary = "查询数据", description = "查询数据")
    public ApiResult<List<List<SearchResp.SearchResult>>> search() {
        MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
                .uri("http://localhost:19530")
                .token("root:Milvus")
                .build());

        List<BaseVector> queryVectors = Arrays.asList(
                new FloatVec(new float[]{0.041732933f, 0.013779674f, -0.027564144f, -0.013061441f, 0.009748648f}),
                new FloatVec(new float[]{0.0039737443f, 0.003020432f, -0.0006188639f, 0.03913546f, -0.00089768134f})
        );
        SearchReq searchReq = SearchReq.builder()
                .collectionName("quick_setup")
                .data(queryVectors)
                .topK(3)
                .build();

        SearchResp searchResp = client.search(searchReq);

        List<List<SearchResp.SearchResult>> searchResults = searchResp.getSearchResults();
        for (List<SearchResp.SearchResult> results : searchResults) {
            System.out.println("TopK results:");
            for (SearchResp.SearchResult result : results) {
                System.out.println(result);
            }
        }
        return ApiResult.success(searchResults);
    }
}

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