婚纱制作网站,重庆市工程招标信息网,毕业设计网站前端代做,沈阳男科医院排名哪家好Elasticsearch8版本增加了KNN向量检索#xff0c;可以基于此功能实现以图搜图功能。
1、首先创建索引#xff0c;es提供了类型为dense_vector的字段#xff0c;用于存储向量#xff0c;其中dims是向量维度#xff0c;可以不配置#xff0c;es会根据第一条插入的向量维度…Elasticsearch8版本增加了KNN向量检索可以基于此功能实现以图搜图功能。
1、首先创建索引es提供了类型为dense_vector的字段用于存储向量其中dims是向量维度可以不配置es会根据第一条插入的向量维度自动配置。
{properties: {file_name: {type: text},feature: {type: dense_vector,dims: 5},number:{type: integer},data_type:{type:keyword}}
}
2、插入10条测试数据 3、通过postman直接进行测试 field向量检索字段名
query_vector输入的向量
k返回得分最高的前几条数据
num_candidates在搜索过程中每个分片考虑的候选邻居的数量
关于参数的具体解释可以看下这篇文章
如何为 kNN 搜索选择最佳 k 和 num_candidates_numcandidates-CSDN博客
4、java api
导入pom dependencygroupIdco.elastic.clients/groupIdartifactIdelasticsearch-java/artifactIdversion8.15.2/version/dependencydependencyartifactIdelasticsearch-rest-client/artifactIdgroupIdorg.elasticsearch.client/groupIdversion8.15.2/version/dependencydependencygroupIdcom.alibaba/groupIdartifactIdfastjson/artifactIdversion2.0.30/version/dependency
测试类
import co.elastic.clients.elasticsearch.ElasticsearchClient;
import co.elastic.clients.elasticsearch._types.SortOrder;
import co.elastic.clients.elasticsearch._types.query_dsl.BoolQuery;
import co.elastic.clients.elasticsearch.core.SearchRequest;
import co.elastic.clients.elasticsearch.core.SearchResponse;
import co.elastic.clients.elasticsearch.core.search.Hit;
import co.elastic.clients.json.jackson.JacksonJsonpMapper;
import co.elastic.clients.transport.ElasticsearchTransport;
import co.elastic.clients.transport.rest_client.RestClientTransport;
import com.alibaba.fastjson.JSONObject;
import org.apache.http.HttpHost;
import org.elasticsearch.client.RestClient;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;public class ElasticsearchKnnTest {public static void main(String[] args) {//获取客户端RestClient restClient RestClient.builder(HttpHost.create(localhost:9200)).build();ElasticsearchTransport transport new RestClientTransport(restClient, new JacksonJsonpMapper());ElasticsearchClient client new ElasticsearchClient(transport);//查询的向量ListFloat queryVector new ArrayList();queryVector.add(0.7F);queryVector.add(0.66F);queryVector.add(1.74F);queryVector.add(1.2F);queryVector.add(0.9F);//取前五个Integer top 5;//最小相似度Double minScore 0.9D;//组装查询条件针对feature字段进行相似向量检索并按照得分排序BoolQuery.Builder builder new BoolQuery.Builder();builder.must(q - q.knn(n - n.field(feature).queryVector(queryVector).k(top).numCandidates(10)));SearchRequest request new SearchRequest.Builder().index(image).minScore(minScore).query(q - q.bool(builder.build())).from(0).size(10).sort(s - s.field(f - f.field(_score).order(SortOrder.Desc))).build();SearchResponse response null;try{response client.search(request, JSONObject.class);}catch (IOException e){e.getStackTrace();}//解析并输出检索结果ListHitJSONObject hits response.hits().hits();for(HitJSONObject hit : hits){JSONObject data hit.source();System.out.println(data.toJSONString() 得分 hit.score());}}
}结果
{number:6,feature:[0.7,0.66,1.74,1.2,0.9],file_name:6.jpg,data_type:aa} 得分0.9999949 {number:2,feature:[0.5,0.3,1.7,1.9,1.8],file_name:66.jpg,data_type:aa} 得分0.9714658 {number:23,feature:[1.7,0.8,1.1,1.5,0.9],file_name:23.jpg,data_type:bb} 得分0.9587538 {number:7,feature:[0.2,0.23,1.7,1.5,0.2],file_name:88.jpg,data_type:cc} 得分0.95746744 {number:99,feature:[0.3,1.2,1.7,0.7,1.9],file_name:9.jpg,data_type:gg} 得分0.949824 {number:5,feature:[0.2,1.3,1.7,1.9,0.2],file_name:77.jpg,data_type:bb} 得分0.94946384 {number:10,feature:[0.1,0.5,1.7,0.7,2.9],file_name:10.jpg,data_type:bb} 得分0.9173416
