ElasticSearch 7.14.0 单机部署及使用

前言

简单的单机 ElasticSearch（全文搜索引擎） 部署 及 ElasticView（可视化管理）部署方式。

快速部署

目录结构

目录结构如下：

[root@lo0-100 elk_7_14_0]# tree
elk_7_14_0
├── docker-compose.yml（查看下方 compose 脚本）
├── es
│   ├── config
│   │   └── elasticsearch.yml（查看下方配置）
│   ├── data（数据文件）
│   │   └── nodes
│   │       └── 0
│   │           ├── indices
│   │           ├── node.lock
│   │           ├── snapshot_cache
│   │           │   ├── segments_5
│   │           │   └── write.lock
│   │           └── _state
│   └── plugins（如果预置了插件，需要先手动复制出来）
├── kibana
│   └── kibana.yml（查看下方配置）
└── esView
    ├── config
    │   └── config.yml（查看下方配置）
    ├── data
    │   ├── es_view.db
    │   └── lock
    └── logs

注意：

  1. 文件夹 elk_7_14_0/es/data​​ 需要赋予权限，否则容器运行时会报权限不足

    chmod 777 <$HOME>/elk_7_14_0/es/data

  2. 在 ElasticSearch 启动后将插件复制到本地

    # 将 <$HOME>/elk_7_14_0/es 替换为自己的目录
docker cp es_7_14_0:/usr/share/elasticsearch/plugins <$HOME>/elk_7_14_0/es

    然后开启 docker-compose.yml 中 es 的插件映射（取消第 22 行的注释）后再重新启动

ElasticSearch

配置文件

elasticsearch.yml 配置文件：

# 集群名称
cluster.name: es-cluster

# 节点名称，可用于识别和区分不同节点
node.name: master

# 是否为主节点
node.master: true

# 是否为数据节点
node.data: true

# 绑定的主机地址，0.0.0.0 表示所有的网络接口都可用
network.host: 0.0.0.0

# 发布的主机地址，用于节点之间的通信，如果不设置，默认与 network.host 相同
# 如果您的机器有多个 IP，需要将它设置为对应的 IP，否则可能会出现通信问题
network.publish_host: 192.168.0.88

# HTTP 协议监听端口
http.port: 9200

# TCP 协议监听端口
transport.tcp.port: 9300

# 是否启用 CORS 跨域访问
http.cors.enabled: true

# 允许的跨域来源，* 表示任意来源
http.cors.allow-origin: "*"

# 设置节点的初始主节点列表
discovery.seed_hosts: ["192.168.0.88:9300"]

# 集群初始化时需要选举的主节点，与 discovery.seed_hosts 配合使用
cluster.initial_master_nodes: ["master"]

# 启用 X-Pack 的用户管理
# 详细查看下方的 开启用户认证
xpack.security.enabled: true

# 启用了 X-Pack 安全功能的同时必须启用 SSL 加密传输
xpack.security.transport.ssl.enabled: true

# 将Elasticsearch进程的内存锁定到物理内存中，以避免在高负载下发生交换而导致性能下降。
bootstrap.memory_lock: true

开启用户认证

在 Elasticsearch 7.14.0 中，X-Pack 的用户管理方式有所变化，现在需要使用内置用户管理，而不是之前的 native realm。具体操作如下：

  1. 修改 elasticsearch.yml​ 文件，添加以下配置：

    xpack.security.enabled: true
xpack.security.transport.ssl.enabled: true

  2. 启动 Elasticsearch，并使用以下命令创建 elastic​ 用户：

    # 管理密码命令
bin/elasticsearch-setup-passwords

# 手动生成（麻烦）
bin/elasticsearch-setup-passwords interactive

# 自动生成（推荐）
bin/elasticsearch-setup-passwords auto

    返回如下：

    [root@4553d80109e8 elasticsearch]# bin/elasticsearch-setup-passwords auto
Initiating the setup of passwords for reserved users elastic,apm_system,kibana,kibana_system,logstash_system,beats_system,remote_monitoring_user.
The passwords will be randomly generated and printed to the console.
Please confirm that you would like to continue [y/N]y


Changed password for user apm_system
PASSWORD apm_system = o8jbA9wrNxDH6d7V775N

Changed password for user kibana_system
PASSWORD kibana_system = 52QmDZ12acTZijNJVOUn

Changed password for user kibana
PASSWORD kibana = 52QmDZ12acTZijNJVOUn

Changed password for user logstash_system
PASSWORD logstash_system = t1uIqLuqu0tC1SxacU9P

Changed password for user beats_system
PASSWORD beats_system = mHrFgNya7V2BxBKJ83Pv

Changed password for user remote_monitoring_user
PASSWORD remote_monitoring_user = 0E8tH68iTaJI8voPrhx7

Changed password for user elastic
PASSWORD elastic = 2hTPGO16XiF51ju4zZXC

    可以调用以下命令修改密码：

    curl -H "Content-Type:application/json" -XPOST -u elastic 'http://127.0.0.1:9200/_xpack/security/user/elastic/_password' -d '{"password":"xxxx"}'

    这是使用 curl 命令通过 HTTP 请求来修改 Elasticsearch 中的 elastic 用户密码的操作。

    • -H "Content-Type:application/json"：指定 HTTP 请求头中的 Content-Type 为 application/json，表示请求体中传递的是 JSON 格式的数据。
    • -XPOST：指定 HTTP 请求方法为 POST。
    • -u elastic：指定使用 elastic 用户名进行身份验证。
    • http://127.0.0.1:9200/_xpack/security/user/elastic/_password：指定请求的 URL，该 URL 用于修改 elastic 用户的密码。
    • -d '{"password":"xxxx"}'：指定请求体中的 JSON 数据，其中包含新密码。
  3. 你将会看到生成的密码，并且该密码将会保存到 Elasticsearch 的 elasticsearch.keystore​ 文件中。

  4. 使用刚刚生成的密码来登录 Elasticsearch

    # 举个例子（请求获取所有的Elasticsearch用户和角色信息，包括他们的权限和密码哈希值，并以美观格式输出。）
curl -XGET -u elastic 'http://127.0.0.1:9200/_xpack/security/user?pretty'

# 返回如下：
{
    "elastic": {
        "username": "elastic",
        "roles": [
            "superuser"
        ],
        "full_name": null,
        "email": null,
        "metadata": {
            "_reserved": true
        },
        "enabled": true
    },
    省略其他信息......
}

参考文档

  1. Elasticsearch 7.x 设置密码访问
  2. Elasticsearch7.7 设置账号密码时的逻辑矛盾问题
  3. ElasticSearch7.14 设置内置用户，使用用户名密码访问

Kibana

kibana.yml 配置文件：

server.name: kibana
server.port: 5601
server.host: 0.0.0.0
server.publicBaseUrl: http://192.168.0.88:5601
i18n.locale: "zh-CN"
# 需x-pack时启用
xpack.monitoring.ui.container.elasticsearch.enabled: true
# es地址（端口对应es配置文件中的 http.port）
elasticsearch.hosts: [ "http://192.168.0.88:9200" ]
# kibana 密码（即访问es的密码）
elasticsearch.username: "elastic"
elasticsearch.password: "2hTPGO16XiF51ju4zZXC"

ElasticView

ElasticView 是一款用来监控 ElasticSearch 状态和操作 ElasticSearch 索引的 web 可视化工具。它由 golang 开发而成，具有部署方便，占用内存小等优点

config.yml 配置文件如下：

log:
  # 日志保留天数
  storageDays: 4
  # 日志保留文件夹
  logDir: "logs"
# 启动端口
port: 8090
# 数据保留类型 分为 sqlite3 和 mysql
dbType: "sqlite3"
# dbType为sqlite3时填 dbPath为数据保存文件地址
sqlite:
  dbPath: "es_view.db"
# dbType为mysql时填
mysql:
  username: "root"
  pwd: "123456"
  ip: "192.168.0.44"
  port: "3306"
  dbName: "es_view"
  maxOpenConns: 10
  maxIdleConns: 10
# jwt 加密密钥
appSecret: "1340691923@qq.com"
# es密码加密密钥 加密方式为 AES
esPwdSecret: "concat_mail!!->1340691923@qq.com"
# ES 版本号
version: "7.14.0"
# 是否为测试模式 如果为 false则打开默认浏览器直接访问地址
deBug: false

默认账号密码为 admin / admin

docker compose

docker-compose.yml 部署脚本如下：

version: '3.0'
services:
  es_7_14_0:
    image: nn200433/elasticsearch:7.14.0
    container_name: es_7_14_0
    environment:
      # 设置集群的发现方式。可选值有 single-node、zen、gce、ec2、azure-classic、azure、s3、kubernetes 等，具体可以参考官方文档。默认为 zen。
      #- discovery.type=single-node
      # 配置时区为中国时区
      # 更多时区配置请参考官方文档：https://www.elastic.co/guide/en/elasticsearch/reference/current/date-math-index-names.html#date-math-index-names
      - TZ=Asia/Shanghai
      # 集群内存限制，JVM初始内存大小为1GB，最大内存大小也为1GB
      # 可根据实际情况进行调整
      # 更多 JVM 相关配置请参考官方文档：https://www.elastic.co/guide/en/elasticsearch/reference/current/jvm-options.html
      - "ES_JAVA_OPTS=-Xms1g -Xmx1g"
    volumes:
      # 数据目录
      - ./es/data:/usr/share/elasticsearch/data
      # 配置文件
      - ./es/config/elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml
      # 插件目录（该镜像已经内置，需自行将插件复制出来！！！）
      #- ./es/plugins:/usr/share/elasticsearch/plugins
    ports:
      - 9200:9200
      - 9300:9300
    # 部分将内存锁定的软限制和硬限制都设置为了 -1，即无限制；
    ulimits:
      memlock:
        soft: -1
        hard: -1
    # 允许容器内的进程使用 mlockall 系统调用
    cap_add:
      - IPC_LOCK
    # 部分将容器内存限制设置为 2GB。
    mem_limit: 2g
    restart: unless-stopped
    networks:
      - net_es

  # ES管理工具1
  esView:
    image: kecikeci/elastic_view:latest
    container_name: es_view
    environment:
      - TZ=Asia/Shanghai
    volumes:
      - ./esView/data:/data
      - ./esView/config:/config
      - ./esView/logs:/logs
    ports:
      - 8090:8090
    restart: unless-stopped
    networks:
      - net_es
    depends_on:
      - es_7_14_0
  
  # ES管理工具2
  cerebro:
    image: lmenezes/cerebro:latest
    container_name: es_cerebro
    environment:
      - TZ=Asia/Shanghai
    ports:
      - 8091:9000
    restart: unless-stopped
    networks:
      - net_es
    depends_on:
      - es_7_14_0

  # ES管理工具3
  kibana:
    image: kibana:7.14.0
    container_name: es_kibana
    environment:
      - TZ=Asia/Shanghai
    volumes:
      - ./kibana/kibana.yml:/usr/share/kibana/config/kibana.yml
    ports:
      - 5601:5601
    restart: unless-stopped
    networks:
      - net_es
    depends_on:
      - es_7_14_0

networks:
  net_es:
    driver: bridge

注：随便选个可视化管理工具就好了。当然，开心也可以全启动。

最终效果

​image​​​

插件

  1. alexklibisz/elastiknn

    诸如 word2vec 和卷积神经网络等方法可以将许多数据形式（文本、图像、用户、品目等）转换为数值向量，以便向量之间的距离计算对应于原始数据的语义相似性。Elasticsearch 是一种普遍使用的搜索解决方案，但它对向量的支持有限。该插件弥补了这一差距，将高效的精确和近似向量搜索引入到 Elasticsearch 中。这使用户可以将传统查询（例如“某个产品”）与向量搜索查询（例如产品的图像（向量））结合起来，以获得更强大的搜索体验。

    这里对应的是 7.14.0 版本，不要下错！！！

ElasticSearch 例子

查看已安装插件

# 方式一：直接访问以下地址
http://192.168.0.88:9200/_cat/plugins

master analysis-hao    7.14.0
master analysis-ik     7.14.0
master analysis-pinyin 7.14.0
master elastiknn       7.14.0.0

# 方式二：服务器敲命令
[root@fa4f68cbccd9 elasticsearch]# elasticsearch-plugin list
analysis-hao
analysis-ik
analysis-pinyin
elastiknn

准备数据

创建索引

PUT elastiknn-index-test
{
    "settings": {
        "number_of_shards": 12,
        "number_of_replicas": 0,
        "analysis": {
            "analyzer": {
                "ik_analyzer": {
                    "type": "custom",
                    "tokenizer": "ik_max_word"
                }
            }
        }
    },
    "mappings": {
        "properties": {
            "title_vec": {
                "type": "elastiknn_dense_float_vector",
                "elastiknn": {
                    "dims": 512,
                    "model": "lsh",
                    "similarity": "cosine",
                    "L": 99,
                    "k": 1
                }
            },
            "title": {
                "type": "text"
            },
            "content": {
                "type": "text"
            }
        }
    }
}

插入几条测试数据

PUT elastiknn-index-test/_doc/1
{
  "title_vec": [0.003425748, 0.0349592, 0.034549166, 0.0022239508, 0.060810618, -0.022429558, -0.07004068, -0.001433827, 0.030713845, 0.011735543, -0.02168091, 0.052747343, -0.003032838, 0.07201349, -0.009121663, -0.035253286, -0.003087171, 0.033998333, -0.033415455, -0.020261392, 0.016096197, -0.055096716, -0.03921775, 3.2432762E-4, 0.048466, -0.020422108, 0.012400874, 0.048314445, 0.022852462, 0.016534192, -0.0791529, 0.04423471, 0.028590905, -0.020271868, -0.037338763, -0.053555593, 0.011092596, -0.026704505, -0.024752602, 0.0011142232, -0.040562928, 0.037982084, 0.0057855425, -0.0049113855, 0.022291979, -0.056476884, 0.029644014, -0.019107254, -0.003555308, -0.009681879, -0.07766024, 0.0070911874, -0.04910793, 0.0027092798, -0.0016745668, -0.028527947, -0.038718343, -0.009667692, -0.020795133, -0.012676213, -0.013904908, -0.017796164, -0.026805168, -0.14529184, 0.005773016, 0.011728189, -0.0047366945, -0.035389133, 0.0019069307, 0.020702973, -0.0055385977, 0.040782254, 0.009009192, 0.040845815, -0.028374115, 0.054288667, -0.011138878, -0.036187347, 0.011442376, -0.0132880295, 0.073753305, -0.097925894, 0.03636818, -0.04072707, 0.012919646, 0.05003554, 0.055168953, -0.012209735, -0.019181276, -0.03257699, -0.058053195, -0.025219388, -0.021497307, -0.001162345, -0.035980176, -0.035884265, -0.033100065, -0.00829503, -0.047079828, -0.02395855, -0.021471633, -0.0038828822, -0.0058413167, -0.015221235, 0.027641736, -0.029010223, 3.8878803E-4, 0.005856648, 2.0493125E-4, 0.02283165, 0.0145955905, 0.11387214, -0.025491737, 0.01645211, 0.061243463, -0.02927204, 0.03987121, 6.2265707E-4, 0.12808461, 0.048372313, -0.04586932, 5.218875E-4, 0.03338346, 0.022554236, -0.017504048, -0.045141526, -0.008534511, -0.017828498, 0.036588904, -0.016354308, 0.007750258, -0.003903245, -1.7327466E-4, -0.0100838, 0.063716546, 0.024896173, 0.0042410623, -0.107530154, 0.0030101123, 0.05138916, -0.003680045, 0.0035677121, 0.03090353, -0.028802726, -0.057706047, -0.0077695334, -0.030067058, -0.0073236115, 0.028622106, -0.020661816, -0.006895617, -0.087078355, 0.020292405, -0.09136877, 0.077871025, 0.034251146, -0.04043043, 0.009749394, -0.060770616, -0.014484504, -0.033462424, -0.04498533, -0.008569162, 0.029500335, -0.02620329, 0.016665585, 0.006792792, -0.110291496, 0.038737524, -0.0316104, -0.08394115, 0.0625756, -0.03423133, -0.029469164, 0.03100374, 0.04309461, -0.074423, -0.028166816, -0.009060677, 0.027572004, 0.005048868, -0.012845816, 0.017110486, 0.015368103, -0.055320587, 0.0064367545, -0.04773569, 0.033720955, 0.07017167, -0.012598084, 0.018722724, -0.033211257, -0.052664973, 0.004498666, -0.023347132, -0.039391857, 0.048897594, 0.029863205, -0.008016134, 0.027602006, 0.027358193, -0.025875986, 0.025465434, -0.02878025, 0.021779213, 0.042077173, -0.011555729, -0.026198916, 0.032100078, -0.043048263, -0.027187653, -0.0402725, -0.020369655, -0.013327858, 1.6972772E-5, -0.003885551, -0.041187145, -0.014162298, 0.028868157, -0.01037627, 0.037233584, -0.018804729, 0.007887838, -0.037073694, -0.05942401, 0.01240634, 0.0221232, -0.028147424, 0.0073892097, 0.03958656, -0.034933493, -0.030155692, 0.009021838, 0.002932108, 0.012281143, 4.1424247E-4, 0.027152574, 0.06452393, 0.023870597, 0.017925534, 0.014357434, -0.106567256, 0.008429156, 0.036298342, -0.028493863, 0.030789679, 0.015373285, 0.0027190328, 0.0011485133, 0.01907994, -0.017132722, -0.015111511, -0.006247805, -0.08744835, 0.020638978, 0.03195406, 0.014723591, 0.015694404, 0.021649327, -3.8856504E-4, -0.004523996, -0.0088082235, 0.019244773, 0.005211741, -0.009596286, -0.012487133, 0.02576933, -0.023590969, -0.07383563, 0.0052784397, -0.019830242, -0.032136276, -0.0559001, -0.027888171, 0.012820674, 0.042624634, 0.020084862, 0.013775034, 0.0018024327, -0.0030583849, 0.008733587, 0.03011925, -0.010819768, 0.01846895, -0.027928192, 0.054508165, 0.025518509, 0.015765147, 0.0055009336, 0.008995844, -0.001397986, 0.013922541, -1.4566258E-4, 0.014703157, 0.00892575, -0.025066845, -0.012928029, -0.0060389563, 0.045801383, 0.018579468, 3.1862292E-4, -0.0035438025, 0.028089356, 0.0033972785, -0.09361243, -0.05980632, 0.040125515, 0.05354448, -0.028163841, 0.05078453, 0.032952312, -0.0362255, -0.040162623, 0.029067608, 0.05469056, -0.057086382, -0.043093912, -0.052694004, -4.773717E-4, 0.010348781, -0.002899653, -0.039878853, -0.0031686411, -0.017416924, 0.0023792672, 0.05185327, 0.052650593, 0.0147140045, 0.00534939, 0.041315783, -0.04911213, 0.015727231, 0.03869164, 0.040212993, -0.027982218, 0.015433941, -0.023373008, -0.0010281625, 0.03179473, 0.0809107, -0.006591896, 0.07492227, 0.051036846, 0.05243999, 0.012217927, -5.8672315E-4, -0.05568167, -0.045633372, 0.004958849, 0.06414878, -0.08234718, 0.055348992, 0.026701692, -0.03219593, -0.031503137, -0.05069124, -0.06783226, 0.0054454487, -0.01793748, -0.048341606, -0.008448702, -7.011754E-4, -0.025374316, 0.048590273, -0.03305374, -0.052704513, 0.052871093, -0.034879833, -0.028176619, -0.008617678, 0.020152742, -0.043748375, 0.066275075, -0.0053646383, -0.020060522, -0.011765275, -0.016720584, -0.11795565, -0.0029911115, -0.06743606, 0.011309156, 8.773381E-4, -0.0075211884, 0.015839778, -0.019497382, 0.0072432347, 0.03325934, -0.038977157, 0.036526572, 0.01263625, 0.031347763, -0.038133346, -0.0046586976, 0.012437723, -0.07406788, -0.026325636, -0.039441943, 0.033739123, -0.02652633, 0.01418932, 0.0115834875, 0.008360267, 0.028494984, 0.010411456, -0.0013066668, -0.02228452, 0.08229267, 0.0063977535, -0.09075892, 0.015774589, -0.012635471, -0.013978461, -0.047774613, -0.036760166, -0.008622342, 0.041786294, 0.031008786, 0.018403547, -0.018363375, -0.03071931, 0.028448794, 0.027587907, -0.031309966, -0.060857788, -0.009144435, -0.009149089, -0.0138160605, -0.023221325, 0.041510608, -0.012421435, -0.0013590785, -0.021834265, 0.0318156, -0.026229814, -0.012133902, 0.021569753, 0.029152462, 0.0062848297, 0.021908771, -0.062652424, -0.019044742, 0.07203413, 0.0645531, 0.064836435, 0.013075323, -0.014966318, -0.013762404, -0.037492, -0.0214157, 0.013161554, 0.003251, 0.04308545, 0.028279785, 0.007946551, -0.023538174, 0.017218951, 0.040116537, 0.0050127236, 0.0068970826, 0.020377496, 0.029326467, 0.03255159, 0.048297945, 7.0397765E-4, 0.017532872, -0.0066716787, -0.03623868, 0.0082028955, 0.007304976, 0.053477094, -0.04715973, 0.026167873, -0.05383834, -0.004187588, 0.006051728, -6.76833E-4, -0.0053422535, -0.009363592, 0.07374679, -0.057454553, -0.0021901669, 0.009387111, -0.057686746, -0.022387888, -0.009012612, 0.025805265, -0.02442889, 0.014319615, -0.006989777, 0.06344666, 0.040135924, -0.012629844, 0.0113826515, 0.044885244, -0.059586156, 0.0048252083, -0.035091937, -0.03562694, -0.040692493, -0.062527485, -0.043084674, 0.009344234, 0.03516395, 0.042935252, -0.041356236, 0.027835313, 5.2603515E-4, 0.0030535415, 0.0016332692, -0.07045216, 0.013352063, 0.012707965],
  "title": "如何在Python中实现图像分类",
  "content": "图像分类是一种机器学习任务，用于将图像分为不同的类别。在Python中，可以使用深度学习框架如TensorFlow或PyTorch来实现图像分类算法。"
}

PUT elastiknn-index-test/_doc/2
{
  "title_vec": [0.0117143085, 0.045121565, 0.09191994, 0.00710566, 0.10173434, -0.008269696, 0.030694166, -0.08023042, 0.040613037, 5.408306E-4, -0.0678575, 0.009960665, 0.017735811, 0.024963507, -0.0032798927, -0.051621184, 0.02022438, 0.06552727, 0.017096853, 0.017405674, -0.0022419635, -0.024945313, -0.06346019, 0.010615516, 0.04126783, 0.007176247, -0.04579749, 0.002776335, -0.0082544815, 0.03610969, -0.0852345, 0.010771294, -0.0029955162, -0.01804644, -0.06935482, -0.018124674, 0.036978412, -0.03874629, -0.068938084, 0.029514022, -0.022372592, -9.625907E-4, 0.024773112, -0.014288258, 0.035850678, -0.05834622, 0.047468852, -0.014909751, -0.013359034, -0.0027257034, -0.003975198, -0.0034708378, 7.885395E-4, 0.04721367, 0.013148114, -0.033280034, -0.026391065, -0.049792975, 0.038625944, -0.033596966, 0.024053974, 0.06774398, -0.033422623, -0.056706488, -0.026988262, -0.033531144, 0.026621416, 0.0075515034, 0.006276171, 0.024225065, -0.034884002, 0.010733044, 0.012259465, 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-0.008704155, 0.0358954, 0.050830826, -0.034152627, 0.054006435, -0.025707228, -1.08596054E-4, -0.06489064, -0.008094756, -0.037734933, 0.012544428, -0.00720663, -0.038805187, 0.031188434, 0.01687598, -0.006368296, 0.05238537, -0.049742915, -0.052619614, -0.027405418, 0.022044819, 0.0057760994, -0.013791722, -0.023093732, -0.035344556, -0.015110196, -0.048774548, -0.018002523, -0.0056335353, 0.066452056, 0.0072636213, 0.0012199358, 0.069724694, 0.005817949, 0.022240497, -0.015321118, 0.0820662, 0.028178591, -0.08539435, 0.0019945367, 0.02006667, -0.0061124275, -5.12594E-4, -0.03549404, -0.00795016, -0.0016249074, -0.012301801, 0.012492642, -5.1937666E-4, -0.016898623, 0.019816255, 0.05134753, 0.018421471, 0.009171907, 0.017838158, 0.043232366, 0.026528297, -0.028303025, -0.016405558, 0.0103685865, -0.031022625, 0.065529704, 0.041098703, -0.034336, -0.05352473, 0.020121042, -0.012044331, 0.0034804214, 0.021412788, -0.026181115, -0.026110137, 0.09393993, 0.0037390857, 0.06305225, -0.037513845, 0.017198782, 0.030384552, 0.02973615, 0.014410185, 0.018699534, 0.044680133, 0.043075275, -0.01910853, 0.004449705, 0.002680912, -0.013235103, 0.036758747, 0.01017896, -0.02270456, -0.03146837, -0.035153992, 0.027544972, -0.0012317606, 0.024501756, 0.059981886, -0.031208033, -0.058444165, -0.0041939807, -0.025207335, 0.01704825, -0.03662345, 0.023225486, -0.057966754, 0.01924193, 0.029117774, -0.006828361, -0.0072594383, 0.0031118144, 0.04780706, -0.020960245, -0.011914286, -0.016184784, -0.07688063, -0.04833374, 0.012004205, 0.005326569, -0.037733167, 0.03464075, 0.030300675, -0.0024853563, -0.00643714, 0.016070087, 0.031160934, -0.0027925444, 0.011567297, -0.043394674, -0.017964955, -0.02457455, 0.009044433, -0.051248875, -0.036129963, 0.02006153, 0.019607674, -0.029285472, -0.06072313, 0.03174668, -0.035128094, -0.012177203, 0.0053611994, -0.01820117, 0.010682981, 0.0079119] ,
  "title": "使用Elasticsearch进行全文搜索",
  "content": "Elasticsearch是一种开源搜索和分析引擎，提供了强大的全文搜索功能。通过使用Elasticsearch的查询语法和过滤器，可以快速地搜索和检索大量文档。"
}

PUT elastiknn-index-test/_doc/3
{
  "title_vec": [0.016784616, 0.020519117, 0.030111508, -0.028519263, 0.07518433, -0.051196612, -0.016427713, -0.03274426, -0.006999862, 0.013230742, 0.0014656008, 0.0106661925, -0.02636794, 0.024049409, -0.021129115, -0.018221525, 0.015097005, 0.06026247, 0.009421245, 0.006248035, 0.03560257, -0.04287118, -0.01708559, 0.02073055, 0.03344506, -0.02307966, -0.0058548474, 0.065074705, -0.008398853, 0.0012660566, -0.09577061, 0.03543353, 0.0024966393, 0.02084241, -0.042799987, -0.06250128, 0.01739693, -0.0025754948, -0.025383381, 0.05294736, -0.07126671, 0.010699218, 0.040031053, -0.0029111512, 0.020760858, -0.028012006, 0.020912662, -0.017607598, -0.036782715, -0.0013501794, -0.035480805, -0.008612508, 0.016199343, -0.016624006, 0.048927076, -0.020968508, -0.02205474, -0.07989428, 0.002516207, 0.027721284, -0.010456964, -0.0017861929, 0.0017328408, -0.02443157, -0.010273274, 0.073678784, 0.012320716, -0.01125913, 0.010977987, 0.014950072, 0.02413759, 0.06941475, -0.010831938, 0.010134839, 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  "title": "构建推荐系统的最佳实践",
  "content": "推荐系统是一种用于向用户提供个性化建议和推荐的技术。构建一个有效的推荐系统需要考虑数据收集、特征工程、算法选择和评估等方面的最佳实践。"
}

搜索测试

前置条件

先弄点词向量，如下：

标  题：如何在Python中实现图像分类
词向量：[0.003425748, 0.0349592, 0.034549166, 0.0022239508, 0.060810618, -0.022429558, -0.07004068, -0.001433827, 0.030713845, 0.011735543, -0.02168091, 0.052747343, -0.003032838, 0.07201349, -0.009121663, -0.035253286, -0.003087171, 0.033998333, -0.033415455, -0.020261392, 0.016096197, -0.055096716, -0.03921775, 3.2432762E-4, 0.048466, -0.020422108, 0.012400874, 0.048314445, 0.022852462, 0.016534192, -0.0791529, 0.04423471, 0.028590905, -0.020271868, -0.037338763, -0.053555593, 0.011092596, -0.026704505, -0.024752602, 0.0011142232, -0.040562928, 0.037982084, 0.0057855425, -0.0049113855, 0.022291979, -0.056476884, 0.029644014, -0.019107254, -0.003555308, -0.009681879, -0.07766024, 0.0070911874, -0.04910793, 0.0027092798, -0.0016745668, -0.028527947, -0.038718343, -0.009667692, -0.020795133, -0.012676213, -0.013904908, -0.017796164, -0.026805168, -0.14529184, 0.005773016, 0.011728189, -0.0047366945, -0.035389133, 0.0019069307, 0.020702973, -0.0055385977, 0.040782254, 0.009009192, 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-0.013762404, -0.037492, -0.0214157, 0.013161554, 0.003251, 0.04308545, 0.028279785, 0.007946551, -0.023538174, 0.017218951, 0.040116537, 0.0050127236, 0.0068970826, 0.020377496, 0.029326467, 0.03255159, 0.048297945, 7.0397765E-4, 0.017532872, -0.0066716787, -0.03623868, 0.0082028955, 0.007304976, 0.053477094, -0.04715973, 0.026167873, -0.05383834, -0.004187588, 0.006051728, -6.76833E-4, -0.0053422535, -0.009363592, 0.07374679, -0.057454553, -0.0021901669, 0.009387111, -0.057686746, -0.022387888, -0.009012612, 0.025805265, -0.02442889, 0.014319615, -0.006989777, 0.06344666, 0.040135924, -0.012629844, 0.0113826515, 0.044885244, -0.059586156, 0.0048252083, -0.035091937, -0.03562694, -0.040692493, -0.062527485, -0.043084674, 0.009344234, 0.03516395, 0.042935252, -0.041356236, 0.027835313, 5.2603515E-4, 0.0030535415, 0.0016332692, -0.07045216, 0.013352063, 0.012707965]

标  题：使用Elasticsearch进行全文搜索
词向量：[0.0117143085, 0.045121565, 0.09191994, 0.00710566, 0.10173434, -0.008269696, 0.030694166, -0.08023042, 0.040613037, 5.408306E-4, -0.0678575, 0.009960665, 0.017735811, 0.024963507, -0.0032798927, -0.051621184, 0.02022438, 0.06552727, 0.017096853, 0.017405674, -0.0022419635, -0.024945313, -0.06346019, 0.010615516, 0.04126783, 0.007176247, -0.04579749, 0.002776335, -0.0082544815, 0.03610969, -0.0852345, 0.010771294, -0.0029955162, -0.01804644, -0.06935482, -0.018124674, 0.036978412, -0.03874629, -0.068938084, 0.029514022, -0.022372592, -9.625907E-4, 0.024773112, -0.014288258, 0.035850678, -0.05834622, 0.047468852, -0.014909751, -0.013359034, -0.0027257034, -0.003975198, -0.0034708378, 7.885395E-4, 0.04721367, 0.013148114, -0.033280034, -0.026391065, -0.049792975, 0.038625944, -0.033596966, 0.024053974, 0.06774398, -0.033422623, -0.056706488, -0.026988262, -0.033531144, 0.026621416, 0.0075515034, 0.006276171, 0.024225065, -0.034884002, 0.010733044, 0.012259465, -0.004880513, 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标  题：构建推荐系统的最佳实践
词向量：[0.016784616, 0.020519117, 0.030111508, -0.028519263, 0.07518433, -0.051196612, -0.016427713, -0.03274426, -0.006999862, 0.013230742, 0.0014656008, 0.0106661925, -0.02636794, 0.024049409, -0.021129115, -0.018221525, 0.015097005, 0.06026247, 0.009421245, 0.006248035, 0.03560257, -0.04287118, -0.01708559, 0.02073055, 0.03344506, -0.02307966, -0.0058548474, 0.065074705, -0.008398853, 0.0012660566, -0.09577061, 0.03543353, 0.0024966393, 0.02084241, -0.042799987, -0.06250128, 0.01739693, -0.0025754948, -0.025383381, 0.05294736, -0.07126671, 0.010699218, 0.040031053, -0.0029111512, 0.020760858, -0.028012006, 0.020912662, -0.017607598, -0.036782715, -0.0013501794, -0.035480805, -0.008612508, 0.016199343, -0.016624006, 0.048927076, -0.020968508, -0.02205474, -0.07989428, 0.002516207, 0.027721284, -0.010456964, -0.0017861929, 0.0017328408, -0.02443157, -0.010273274, 0.073678784, 0.012320716, -0.01125913, 0.010977987, 0.014950072, 0.02413759, 0.06941475, -0.010831938, 0.010134839, 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----------------------------------------------------

标  题：Python中的图像分类算法实现
词向量：[0.018334504, 0.024280138, 0.060270008, -0.00703231, 0.062384542, -0.033714343, -0.089470446, 0.01052925, 0.030128203, 0.016259717, -0.0041708597, 0.07508473, -1.4808029E-5, 0.07208159, -0.025966516, -0.035937995, -0.019125087, 0.02894972, -0.042877838, -0.016556587, 0.038519125, -0.05497628, -0.022371262, 0.0052025816, 0.06263389, -0.012726134, -0.0076508494, 0.048508916, 0.028581567, 0.025932636, -0.04068385, 0.03526424, 0.043063276, -0.0045921654, -0.03677957, -0.044359524, 0.022593407, -0.010674483, -0.030666064, 0.0104613975, -0.06754987, 0.03236309, 7.70517E-4, -0.017036248, 0.02123662, -0.054729816, 0.02336933, -0.028074302, 0.0039182375, -0.012599534, -0.097433954, 0.003237031, -0.037144624, 0.0058577955, -0.006290964, -0.025053108, -0.056105237, -0.008978243, -0.02688102, -0.0129503645, -0.008121182, -0.029434739, -0.022789279, -0.15444307, 0.014734943, 0.0065265135, 0.011842373, -0.036312938, 0.008875027, 0.03323997, 0.010595138, 0.06085125, -0.008255827, 0.042788893, 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标  题：利用Elasticsearch进行高效全文搜索
词向量：[0.028334545, 0.04808455, 0.054223213, -0.021798454, 0.0945059, 0.0033262551, 5.0448696E-5, -0.056805007, 0.037401047, 0.010768487, -0.048363864, 0.025941702, 0.023150815, 0.04304203, -0.031488836, -0.057050273, -0.018632913, 0.06198032, 0.018015359, 0.021398744, 0.019701745, -0.03146597, -0.056198522, 0.013590538, 0.046737146, -0.009726907, -0.042173695, 0.0068503506, 0.03534159, 0.03005312, -0.064686924, 0.03621582, -0.016471885, -0.012193312, -0.074417986, -0.01766112, 0.02724811, -0.038141087, -0.049351376, 0.052266356, -0.040434923, -0.018830702, 0.005368009, -0.03396543, 0.015351972, -0.077662274, 0.06817002, -0.0035892013, 0.018406149, 0.02260536, -0.023874182, 0.0011654581, 0.018005168, 0.051917426, 0.010722944, -0.04200844, -0.04632208, -0.07842748, 0.016677842, -0.024655232, 0.024934692, 0.036638416, -0.04926346, -0.059437174, -0.020153923, -0.03533702, 0.0072743865, 0.008430382, 0.008015532, 0.060297247, 0.010504731, 0.03086903, 0.012021666, -0.034335937, -0.009965666, 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标  题：推荐系统构建的最佳实践指南
词向量：[0.020176865, 0.018013671, 0.020441825, -0.00817161, 0.07331785, -0.051497992, -0.008001804, -0.009821307, 0.01772255, 0.01185993, -0.033124562, 0.03846657, 0.015009669, 0.040160175, -0.036490418, -0.05288429, 0.017452706, 0.07770491, 1.2476006E-4, -0.016186768, 0.007361202, -0.047650225, -0.044357236, 0.0041307276, 0.011872713, -0.017730616, -0.023548584, 0.021989651, 9.2729804E-4, -0.0031401685, -0.10814663, 0.049524203, 0.028511299, -0.014449703, -0.03917067, -0.050791513, -0.00372236, -0.012571809, -0.037137218, 0.057121474, -0.019856542, 0.031745452, -0.013830771, 0.020373918, 0.024949305, -0.02923703, 0.034603216, -0.012404621, -0.0072062183, -0.0017996699, -0.05231095, -0.012444853, -0.007687434, 0.012115218, 0.04647616, -0.030036885, -0.035796095, -0.017427383, 0.0035970088, 0.0039387187, -0.003913366, 0.017365806, -0.004058628, -0.039199222, 0.011451096, 0.0407556, -0.0052178977, -0.017166857, -3.721325E-4, 0.021847943, 0.016629001, 0.024921775, 0.008685851, 0.02907511, 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近邻查询

Elastiknn 提供了一个名为 elastiknn\_nearest\_neighbors 的查询，它可以像标准 Elasticsearch 查询一样在 GET /_search​ 请求中使用，也可以与标准 Elasticsearch 查询结合使用。

使用问题：Python 中的图像分类算法实现

使用以下命令搜索：

GET /elastiknn-index-test/_search
{
    "_source": [
        "title",
        "content"
    ],
    "query": {
        "elastiknn_nearest_neighbors": {
            "field": "title_vec",
            "vec": {
                "values": [0.018334504, 0.024280138, 0.060270008, -0.00703231, 0.062384542, -0.033714343, -0.089470446, 0.01052925, 0.030128203, 0.016259717, -0.0041708597, 0.07508473, -1.4808029E-5, 0.07208159, -0.025966516, -0.035937995, -0.019125087, 0.02894972, -0.042877838, -0.016556587, 0.038519125, -0.05497628, -0.022371262, 0.0052025816, 0.06263389, -0.012726134, -0.0076508494, 0.048508916, 0.028581567, 0.025932636, -0.04068385, 0.03526424, 0.043063276, -0.0045921654, -0.03677957, -0.044359524, 0.022593407, -0.010674483, -0.030666064, 0.0104613975, -0.06754987, 0.03236309, 7.70517E-4, -0.017036248, 0.02123662, -0.054729816, 0.02336933, -0.028074302, 0.0039182375, -0.012599534, -0.097433954, 0.003237031, -0.037144624, 0.0058577955, -0.006290964, -0.025053108, -0.056105237, -0.008978243, -0.02688102, -0.0129503645, -0.008121182, -0.029434739, -0.022789279, -0.15444307, 0.014734943, 0.0065265135, 0.011842373, -0.036312938, 0.008875027, 0.03323997, 0.010595138, 0.06085125, 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            },
            "model": "lsh",
            "similarity": "cosine",
            "candidates": 50
        }
    }
}

响应结果如下：

{
    "took": 4,
    "timed_out": false,
    "_shards": {
        "total": 12,
        "successful": 12,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 3,
            "relation": "eq"
        },
        "max_score": 1.9501064,
        "hits": [
            {
                "_index": "elastiknn-index-test",
                "_type": "_doc",
                "_id": "1",
                "_score": 1.9501064,
                "_source": {
                    "title": "如何在Python中实现图像分类",
                    "content": "图像分类是一种机器学习任务，用于将图像分为不同的类别。在Python中，可以使用深度学习框架如TensorFlow或PyTorch来实现图像分类算法。"
                }
            },
            {
                "_index": "elastiknn-index-test",
                "_type": "_doc",
                "_id": "3",
                "_score": 1.564908,
                "_source": {
                    "title": "构建推荐系统的最佳实践",
                    "content": "推荐系统是一种用于向用户提供个性化建议和推荐的技术。构建一个有效的推荐系统需要考虑数据收集、特征工程、算法选择和评估等方面的最佳实践。"
                }
            },
            {
                "_index": "elastiknn-index-test",
                "_type": "_doc",
                "_id": "2",
                "_score": 1.4582255,
                "_source": {
                    "title": "使用Elasticsearch进行全文搜索",
                    "content": "Elasticsearch是一种开源搜索和分析引擎，提供了强大的全文搜索功能。通过使用Elasticsearch的查询语法和过滤器，可以快速地搜索和检索大量文档。"
                }
            }
        ]
    }
}

在过滤后的文档集上进行近邻查询

使用问题：Python 中的图像分类算法实现

使用以下命令搜索：

GET /elastiknn-index-test/_search
{
    "size": 10,
    "_source": [
        "title",
        "content"
    ],
    "query": {
        "function_score": {
            "query": {
                "bool": {
                    "should": [
                        {
                            "match_phrase": {
                                "title": {
                                    "query": "图像分类算法"
                                }
                            }
                        },
                        {
                            "match_phrase": {
                                "content": {
                                    "query": "图像分类算法"
                                }
                            }
                        }
                    ]
                }
            },
            "functions": [
                {
                    "elastiknn_nearest_neighbors": {
                        "field": "title_vec",
                        "model": "exact",
                        "similarity": "cosine",
                        "vec": {
                            "values": [0.018334504, 0.024280138, 0.060270008, -0.00703231, 0.062384542, -0.033714343, -0.089470446, 0.01052925, 0.030128203, 0.016259717, -0.0041708597, 0.07508473, -1.4808029E-5, 0.07208159, -0.025966516, -0.035937995, -0.019125087, 0.02894972, -0.042877838, -0.016556587, 0.038519125, -0.05497628, -0.022371262, 0.0052025816, 0.06263389, -0.012726134, -0.0076508494, 0.048508916, 0.028581567, 0.025932636, -0.04068385, 0.03526424, 0.043063276, -0.0045921654, -0.03677957, -0.044359524, 0.022593407, -0.010674483, -0.030666064, 0.0104613975, -0.06754987, 0.03236309, 7.70517E-4, -0.017036248, 0.02123662, -0.054729816, 0.02336933, -0.028074302, 0.0039182375, -0.012599534, -0.097433954, 0.003237031, -0.037144624, 0.0058577955, -0.006290964, -0.025053108, -0.056105237, -0.008978243, -0.02688102, -0.0129503645, -0.008121182, -0.029434739, -0.022789279, -0.15444307, 0.014734943, 0.0065265135, 0.011842373, -0.036312938, 0.008875027, 0.03323997, 0.010595138, 0.06085125, -0.008255827, 0.042788893, -0.029874694, 0.051551364, -0.03500654, -0.026431937, 0.0052033635, -0.015055023, 0.08952935, -0.091231525, 0.027016645, -0.053308353, 0.005032279, 0.06008603, 0.07357959, -0.009609652, -0.003973632, -0.057170775, -0.04671027, -0.0048645018, -0.033610284, -0.003089503, -0.04178789, -0.0133626815, -0.040106755, -0.01489888, -0.04663396, -0.007279365, -0.02141951, -0.0034206498, -0.0060589705, -0.012498243, 0.018241506, -0.025400665, 0.008988069, 0.00954205, 0.0067822053, 0.026373647, 0.022600269, 0.11963017, -0.03573345, 0.01343307, 0.070174664, -0.040029693, 0.035598405, -0.004611507, 0.11729125, 0.059418842, -0.034857914, 0.017056938, 0.035486206, 0.031676758, -0.029388057, -0.054247927, -0.0048671667, -0.011214918, 0.046272412, -0.043653212, 0.005927232, -0.0060632927, -0.031766295, -0.0065160003, 0.06309202, 0.031696334, 0.010834883, -0.11858474, 0.015118523, 0.063034624, 0.0037512407, 0.019837465, 0.03365307, -0.04349621, -0.04838277, -0.014911847, -0.027467359, -0.03379531, 0.013083895, -0.023289312, -0.0122351255, -0.054968126, 0.027260402, -0.11570955, 0.0810923, 0.048150994, -0.02272245, 0.02167532, -0.07473878, 0.010024956, -0.045342594, -0.040995996, -0.003830455, 0.029282497, -0.031038662, 0.003926054, 0.015166712, -0.103414126, 0.02164625, -0.032480583, -0.06634523, 0.05898234, -0.03116015, -0.056782607, 0.025620563, 0.046223305, -0.068947636, -0.036224153, -0.0054390104, 0.034677997, -0.0012064547, -0.014691091, 0.016825436, 0.026014788, -0.046168473, 0.0064939386, -0.065640055, 0.044646576, 0.081166275, -0.0035780836, 0.022505274, -0.017853433, -0.039874192, 0.0027993673, -0.018770952, -0.02639674, 0.03317332, 0.051389776, -0.0042318776, 0.030204441, 0.013347908, -0.011683809, 0.03636062, -0.040875424, 0.012521518, 0.051372528, -0.019215906, -0.014054019, 0.025017811, -0.023925602, -0.040116623, -0.05494136, -0.015616297, -0.021807574, -0.015647572, -0.002124208, -0.051249743, -0.0096973395, 0.031446666, -0.02780494, 0.049189955, -0.029717067, 0.007830184, -0.05809309, -0.058707375, 0.030614134, 0.03524412, -0.034313593, 0.0112889055, 0.037618678, -0.033219296, -0.03769533, 0.005770336, -0.0017073594, -0.0040953867, 0.0075088665, 0.032104407, 0.038916063, 0.028482001, 0.018831132, 0.0196853, -0.0968549, 0.010079594, 0.024128614, -0.019420033, 0.053984404, -0.003876352, 0.009392947, 0.0022547024, 0.020174231, 0.0020515497, -0.003640826, -0.008331576, -0.09186866, 0.035954855, 0.03892597, 0.008403802, 0.032515895, 0.020249581, -0.015331016, -0.0033263185, 0.020847946, 0.024370752, -0.013341614, -0.025442561, -0.022106968, 0.033984475, -0.038147148, -0.06698335, -0.0025435148, -0.025766112, -0.02414459, -0.06505771, -0.035145413, 0.003635327, 0.06367122, -0.010155436, -0.0024379725, -4.5812075E-4, 0.018759023, 0.026726952, 0.024773588, -0.010748584, 0.02538642, -0.026085567, 0.047703348, 0.014363001, 0.009833401, 0.003947038, -0.0067513445, -0.0025483482, 0.020491373, -0.006104005, 0.02506443, 0.012511464, -0.0024656195, 0.0031949526, -1.6820827E-4, 0.021071967, 0.025954686, 0.019704795, -0.014183586, 0.045486882, 0.02204026, -0.07147743, -0.06660837, 0.032999154, 0.052447073, -0.017600073, 0.05710723, 0.028700078, -0.02923506, -0.04366008, 0.041560367, 0.055844158, -0.032798912, -0.05667255, -0.04946596, 0.003996133, -0.0025304968, 0.027621573, -0.042329058, -0.016636109, -0.04114112, -0.02273995, 0.05618336, 0.053753596, 0.018957214, -0.006487004, 0.04412349, -0.024048153, 0.017044146, 0.040858924, 0.040573496, -0.019988013, 0.019975705, -0.038978666, -0.015306521, 0.039337978, 0.09774831, -0.006554067, 0.08606857, 0.0585227, 0.05483377, 0.014958298, -0.0048144795, -0.037498493, -0.06519725, 0.003035377, 0.05896656, -0.0849043, 0.048408963, 0.021710442, -0.034050927, -0.0039758496, -0.07412198, -0.044429187, -0.008626837, -0.010930866, -0.048836604, 0.0011481331, -0.005427692, -0.018389411, 0.05359212, -0.026096955, -0.050659325, 0.059301037, -0.032735784, -0.030279418, -0.0030599472, 0.0142146405, -0.037663408, 0.0737027, -0.01956614, -0.037615348, -1.8160511E-4, -0.029712649, -0.089713134, 0.0011612815, -0.08236109, -0.00709404, 3.5659302E-4, -0.020743284, 0.016616344, -0.028921027, 0.009294537, 0.017093465, -0.032445095, 0.058222927, 0.008568909, 0.03677239, -0.03746566, 0.014174925, 0.03566817, -0.063097134, -0.019889072, -0.054829415, 0.03446317, -0.023996752, 0.008673901, 0.01951796, 0.005942422, 0.014864499, 0.0055181044, 0.007302453, -0.016944436, 0.10389743, -0.0020791213, -0.07125883, 0.0352342, -0.012946072, -0.021096097, -0.055850707, -0.04794835, -0.006866279, 0.021536127, 0.024503617, 0.017718377, -0.022436446, -0.027201625, 0.04263096, 0.023965012, -0.03678662, -0.08512502, 0.0036935005, -0.012211001, -0.031326313, -0.032609697, 0.06252547, -0.00652635, 0.016881183, -0.035671808, 0.0143922, -0.026964137, -0.022093501, 0.027231608, 0.0348811, 0.012951652, 0.035395734, -0.06288116, -0.024185633, 0.057561185, 0.055526968, 0.0584887, -9.907435E-4, -0.02232988, -0.016641524, -0.036239166, -0.008777217, 0.012492639, 0.0052368673, 0.03817985, 0.05986094, 0.005637845, -0.031989623, 0.032398615, 0.051869143, 0.0113028465, 0.0143419765, 0.012046146, 0.028136974, 0.027474927, 0.045836695, 0.015627034, 0.016563581, -0.02871797, -0.045672912, 0.0069116773, -0.004560964, 0.06710901, -0.057497114, 0.041751735, -0.04781406, -0.0069232797, 0.030780569, -0.00906503, 0.0020974206, 0.02144345, 0.081875205, -0.061759055, 0.0039352537, 0.0100662345, -0.060573794, -0.008153452, -0.013763209, 0.05385816, -0.016150707, 0.013022631, 0.0014990036, 0.06283503, 0.04291183, -0.028426409, 0.0060491026, 0.0480245, -0.076360986, -0.018043978, -0.026324745, -0.060814414, -0.025072854, -0.047528446, -0.057492107, 0.0045749904, 0.03887366, 0.020883841, -0.051129226, 0.034032185, 0.01762571, 0.009605042, -0.00511932, -0.091090634, 0.0070879753, 0.0021814427]
                        }
                    }
                }
            ]
        }
    }
}

响应结果如下：

{
    "took": 2,
    "timed_out": false,
    "_shards": {
        "total": 12,
        "successful": 12,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 1,
            "relation": "eq"
        },
        "max_score": 3.3932827,
        "hits": [
            {
                "_index": "elastiknn-index-test",
                "_type": "_doc",
                "_id": "1",
                "_score": 3.3932827,
                "_source": {
                    "title": "如何在Python中实现图像分类",
                    "content": "图像分类是一种机器学习任务，用于将图像分为不同的类别。在Python中，可以使用深度学习框架如TensorFlow或PyTorch来实现图像分类算法。"
                }
            }
        ]
    }
}

注：当使用 model:lsh​ 时，"candidates" 参数被忽略，向量不会像 elastiknn_nearest_neighbors 查询那样用精确的相似度重新评分。相反，得分是：max similarity score * proportion of matching hashes​ 。这是一个必要的结果，因为评分函数接受一个文档 ID，必须立即返回一个分数。

使用查询重构器

使用问题：Python 中的图像分类算法实现

使用以下命令搜索：

GET /elastiknn-index-test/_search
{
    "size": 10,
    "_source": [
        "title",
        "content"
    ],
    "query": {
        "bool": {
            "should": [
                {
                    "match_phrase": {
                        "title": {
                            "query": "图像分类算法"
                        }
                    }
                },
                {
                    "match_phrase": {
                        "content": {
                            "query": "图像分类算法"
                        }
                    }
                }
            ]
        }
    },
    "rescore": {
        "window_size": 10,
        "query": {
            "rescore_query": {
                "elastiknn_nearest_neighbors": {
                    "field": "title_vec",
                    "model": "exact",
                    "similarity": "cosine",
                    "vec": {
                        "values": [0.018334504, 0.024280138, 0.060270008, -0.00703231, 0.062384542, -0.033714343, -0.089470446, 0.01052925, 0.030128203, 0.016259717, -0.0041708597, 0.07508473, -1.4808029E-5, 0.07208159, -0.025966516, -0.035937995, -0.019125087, 0.02894972, -0.042877838, -0.016556587, 0.038519125, -0.05497628, -0.022371262, 0.0052025816, 0.06263389, -0.012726134, -0.0076508494, 0.048508916, 0.028581567, 0.025932636, -0.04068385, 0.03526424, 0.043063276, -0.0045921654, -0.03677957, -0.044359524, 0.022593407, -0.010674483, -0.030666064, 0.0104613975, -0.06754987, 0.03236309, 7.70517E-4, -0.017036248, 0.02123662, -0.054729816, 0.02336933, -0.028074302, 0.0039182375, -0.012599534, -0.097433954, 0.003237031, -0.037144624, 0.0058577955, -0.006290964, -0.025053108, -0.056105237, -0.008978243, -0.02688102, -0.0129503645, -0.008121182, -0.029434739, -0.022789279, -0.15444307, 0.014734943, 0.0065265135, 0.011842373, -0.036312938, 0.008875027, 0.03323997, 0.010595138, 0.06085125, -0.008255827, 0.042788893, -0.029874694, 0.051551364, -0.03500654, -0.026431937, 0.0052033635, -0.015055023, 0.08952935, -0.091231525, 0.027016645, -0.053308353, 0.005032279, 0.06008603, 0.07357959, -0.009609652, -0.003973632, -0.057170775, -0.04671027, -0.0048645018, -0.033610284, -0.003089503, -0.04178789, -0.0133626815, -0.040106755, -0.01489888, -0.04663396, -0.007279365, -0.02141951, -0.0034206498, -0.0060589705, -0.012498243, 0.018241506, -0.025400665, 0.008988069, 0.00954205, 0.0067822053, 0.026373647, 0.022600269, 0.11963017, -0.03573345, 0.01343307, 0.070174664, -0.040029693, 0.035598405, -0.004611507, 0.11729125, 0.059418842, -0.034857914, 0.017056938, 0.035486206, 0.031676758, -0.029388057, -0.054247927, -0.0048671667, -0.011214918, 0.046272412, -0.043653212, 0.005927232, -0.0060632927, -0.031766295, -0.0065160003, 0.06309202, 0.031696334, 0.010834883, -0.11858474, 0.015118523, 0.063034624, 0.0037512407, 0.019837465, 0.03365307, -0.04349621, -0.04838277, -0.014911847, -0.027467359, -0.03379531, 0.013083895, -0.023289312, -0.0122351255, -0.054968126, 0.027260402, -0.11570955, 0.0810923, 0.048150994, -0.02272245, 0.02167532, -0.07473878, 0.010024956, -0.045342594, -0.040995996, -0.003830455, 0.029282497, -0.031038662, 0.003926054, 0.015166712, -0.103414126, 0.02164625, -0.032480583, -0.06634523, 0.05898234, -0.03116015, -0.056782607, 0.025620563, 0.046223305, -0.068947636, -0.036224153, -0.0054390104, 0.034677997, -0.0012064547, -0.014691091, 0.016825436, 0.026014788, -0.046168473, 0.0064939386, -0.065640055, 0.044646576, 0.081166275, -0.0035780836, 0.022505274, -0.017853433, -0.039874192, 0.0027993673, -0.018770952, -0.02639674, 0.03317332, 0.051389776, -0.0042318776, 0.030204441, 0.013347908, -0.011683809, 0.03636062, -0.040875424, 0.012521518, 0.051372528, -0.019215906, -0.014054019, 0.025017811, -0.023925602, -0.040116623, -0.05494136, -0.015616297, -0.021807574, -0.015647572, -0.002124208, -0.051249743, -0.0096973395, 0.031446666, -0.02780494, 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                    }
                }
            },
            "query_weight": 0,
            "rescore_query_weight": 1
        }
    }
}

响应结果如下：

{
    "took": 2,
    "timed_out": false,
    "_shards": {
        "total": 12,
        "successful": 12,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 1,
            "relation": "eq"
        },
        "max_score": 1.9501064,
        "hits": [
            {
                "_index": "elastiknn-index-test",
                "_type": "_doc",
                "_id": "1",
                "_score": 1.9501064,
                "_source": {
                    "title": "如何在Python中实现图像分类",
                    "content": "图像分类是一种机器学习任务，用于将图像分为不同的类别。在Python中，可以使用深度学习框架如TensorFlow或PyTorch来实现图像分类算法。"
                }
            }
        ]
    }
}

Java 接入

待更新....

‍

posted @ 2023-06-09 16:38:00 猎隼丶止戈 阅读(46) 评论(0)
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