所以我们有两种情况：
1.主页上的单个搜索输入
1.具有多个输入字段的订单表单
ResultitemsHomepage single searchfieldOrderingform multiple search fields
所以总结一下：
1.在我们的网站上，用户搜索公司，选择一个，然后订购。
1.我们要做一个全球性的搜索领域，在主页上。我们有一个订单，我们有4个输入字段;公司名称，公司编号，公司地址（最常见的街道），公司城市。
- city、法律的_form和联邦_state上的聚合，用户可将其用作筛选器
1.总体思路是：用户登陆主页或订购表单页面，执行搜索，过滤，然后找到公司，单击公司结果->这将在订购表单中加载公司详细信息，以便用户可以订购。
问：
1.我感到困惑的是，自动完成功能如何在一个全局搜索字段和一个有4个（或5个）字段的订购表单上工作
1.你能帮助我们使用我们的场景查询的例子吗？
我们做过的事情：
1.将字段复制到通用字段，以便我们可以查询单个字段（例如，街道，邮政编码，城市合并在1个full_address字段中）
1.创建了一个自定义分析器，用于分析德国街道名称和语音
建议事项：主页搜索
1.单个搜索字段应搜索all_names、all_address、company_number、all_offices（=所有城市），并返回最高相关性
1.通过调整查询中的“weight”和“boost”，可以部分实现最高相关性
1.然而，我不明白如何“搜索，因为你类型”完成将工作，当用户在1搜索字段输入不同的条款（如作为示例名称+邮政编码+城市）
1.自动完成功能只对前三个字母有效吗？请解释。
建议事项：订单搜索
1.订单有4个字段;公司名称、公司编号、地址、城市
1.理想情况下，当用户输入时，他应该在他输入的字段上获得自动完成
1.这很简单; name字段=名称建议等。
1.当多个字段被填充，自动完成应该只匹配所有输入字段的建议？像公司名称的一部分被填充，然后地址自动完成只从匹配的公司名称？我们应该想要这个吗？
1.我们是否应该在订购表格中添加第五个全球搜索字段，就像主页上的那样？
示例分析器和Map：
{
"settings": {
"index": {
"number_of_shards": 1,
"number_of_replicas": 0
},
"analysis": {
"filter": {
"german_stop": {
"type": "stop",
"stopwords": "_german_"
},
"german_stemmer": {
"type": "stemmer",
"language": "light_german"
},
"snowball": {
"type": "snowball",
"language": "German2"
},
"german_phonetic": {
"type": "phonetic",
"encoder": "koelnerphonetik",
"replace": false
},
"address_synonyms": {
"type": "synonym",
"synonyms": [ "str, strasse, straße => strass" ]
}
},
"analyzer": {
"names_analyzer": {
"type": "custom",
"tokenizer": "standard",
"char_filter": [
"html_strip"
],
"filter": [
"lowercase",
"word_delimiter",
"german_normalization",
"german_phonetic",
"asciifolding",
"apostrophe"
]
},
"address_analyzer": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"german_normalization",
"german_stop",
"snowball",
"german_stemmer",
"address_synonyms",
"german_phonetic",
"asciifolding",
"apostrophe",
"word_delimiter"
]
},
"keyword_analyzer": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"german_normalization",
"asciifolding"
]
}
}
}
},
"mappings": {
"properties": {
"all_names":{
"type":"text",
"analyzer": "names_analyzer",
"fields":{
"search-as-you-type":{
"type":"search_as_you_type"
}
}
},
"name":{
"type":"text",
"analyzer": "names_analyzer",
"copy_to": "all_names"
},
"alt_names":{
"type":"nested",
"include_in_root": true,
"properties":{
"name": {
"type": "text",
"analyzer": "names_analyzer",
"copy_to": "all_names" },
"office":{
"type": "keyword",
"copy_to": "all_offices",
"fields":{
"text":{
"type":"text"
},
"keyword_analyzed":{
"type":"text",
"analyzer":"keyword_analyzer"
}
}
}
}
},
"branches":{
"type":"nested",
"include_in_root": true,
"properties":{
"name": {
"type": "text",
"analyzer": "names_analyzer",
"copy_to": "all_names"
},
"office":{
"type": "keyword",
"copy_to": "all_offices",
"fields":{
"text":{
"type":"text"
},
"keyword_analyzed":{
"type":"text",
"analyzer":"keyword_analyzer"
}
}
}
}
},
"full_address":{
"type":"text",
"analyzer": "address_analyzer",
"fields":{
"search-as-you-type":{
"type":"search_as_you_type"
}
}
},
"all_address":{
"type":"text",
"analyzer": "address_analyzer",
"fields":{
"search-as-you-type":{
"type":"search_as_you_type"
}
}
},
"street":{
"type":"text",
"analyzer": "address_analyzer",
"copy_to": "all_address",
"fields":{
"search-as-you-type":{
"type":"search_as_you_type"
}
}
},
"zipcode":{
"type":"keyword",
"copy_to": "all_address",
"fields":{
"search-as-you-type":{
"type":"search_as_you_type"
},
"text": {
"type":"text"
}
}
},
"city":{
"type":"keyword",
"copy_to": [ "all_offices", "all_address" ],
"fields":{
"search-as-you-type":{
"type":"search_as_you_type"
},
"text":{
"type":"text",
"analyzer": "address_analyzer"
}
}
},
"legal_form":{
"type":"keyword",
"fields":{
"search-as-you-type":{
"type":"search_as_you_type"
},
"text":{
"type":"text"
}
}
},
"all_offices":{
"type":"keyword",
"fields":{
"search-as-you-type":{
"type":"search_as_you_type"
},
"text":{
"type":"text"
}
}
},
"office":{
"type":"keyword",
"copy_to": "all_offices"
},
"registrar":{
"type":"keyword",
"copy_to": "all_offices"
},
"former_registrar":{
"type":"keyword",
"copy_to": "all_offices"
},
"state":{
"type":"keyword",
"fields":{
"text":{
"type":"text"
}
}
},
"company_number":{
"type":"keyword",
"fields":{
"search-as-you-type":{
"type":"search_as_you_type"
},
"text":{
"type":"text"
}
}
},
"status":{
"type":"keyword",
"fields":{
"text":{
"type":"text"
}
}
}
}
}
}字符串
全局搜索查询示例：
{
"query": {
"bool": {
"should": [
{
"multi_match": {
"query": "Hans-Sachs-Straße",
"fuzziness": "AUTO",
"minimum_should_match": "60%"
}
}
],
"filter": [
{ "term": { "state": "" }},
{ "term": { "all_offices": "" }}
]
}
},
"highlight": {
"fields": {
"name": {},
"full_address": {},
"office": {},
"company_number": {},
"registrar": {},
"native_company_number": {}
}
},
"aggs": {
"states": {
"terms": { "field": "state" }
},
"city": {
"terms": { "field": "all_offices" }
}
}
}型
订单查询示例：
{
"query": {
"bool": {
"should": [
{
"multi_match": {
"query": "str",
"minimum_should_match": "60%",
"boost": 2,
"fields": [
"full_address.search-as-you-type",
"full_address.search-as-you-type._2gram",
"full_address.search-as-you-type._3gram"
]
}
},
{
"multi_match": {
"query": "HR",
"minimum_should_match": "60%",
"boost": 5,
"fields": [
"company_number.search-as-you-type",
"company_number.search-as-you-type._2gram",
"company_number.search-as-you-type._3gram"
]
}
},
{
"multi_match": {
"query": "DK",
"minimum_should_match": "60%",
"boost": 5,
"fields": [
"all_names.search-as-you-type",
"all_names.search-as-you-type._2gram",
"all_names.search-as-you-type._3gram"
]
}
},
{
"multi_match": {
"query": "Dus",
"minimum_should_match": "60%",
"boost": 3,
"fields": [
"all_offices.search-as-you-type",
"all_offices.search-as-you-type._2gram",
"all_offices.search-as-you-type._3gram"
]
}
}
]
},
"filter": [
{ "term": { "state": "" }},
{ "term": { "all_offices": "" }}
]
},
"highlight": {
"fields": {
"name": {},
"full_address": {},
"office": {},
"company_number": {},
"registrar": {},
"native_company_number": {}
}
},
"aggs": {
"states": {
"terms": { "field": "state" }
},
"city": {
"terms": { "field": "all_offices" }
}
}
}型
1条答案
按热度按时间jq6vz3qz1#
这就是自动完成如何为多个字段工作，具有不同的
fields数据类型。我在这些字段上启用了
analyzers和search_analyzers，所以我利用了子字符串（基于n-gram tokenziers）搜索。_settings创建索引：字符串
_mappings为您的索引：型
一旦您将数据插入索引中。现在，让我们介绍
search-ui：https://docs.elastic.co/search-ui/tutorials/elasticsearch型