这个问题类似于我的另一个问题，在这里输入链接描述，Val回答了这个问题。
我有一个包含 3 个文档的索引。
{
"firstname": "Anne",
"lastname": "Borg",
}
{
"firstname": "Leanne",
"lastname": "Ray"
},
{
"firstname": "Anne",
"middlename": "M",
"lastname": "Stone"
}
当我搜索"Ann"时，我希望 elastic 返回所有这 3 个文档(因为它们都在一定程度上与术语"Ann"匹配(。但是，我希望Leanne Ray的分数(相关性排名(较低，因为搜索词"Ann"在本文档中出现的位置比其他两个文档中的要晚。
这是我的索引设置...
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"filter": [
"lowercase"
],
"type": "custom",
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"token_chars": [
"letter",
"digit",
"custom"
],
"custom_token_chars": "'-",
"min_gram": "1",
"type": "ngram",
"max_gram": "2"
}
}
}
},
"mappings": {
"properties": {
"firstname": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
},
"copy_to": [
"full_name"
]
},
"lastname": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
},
"copy_to": [
"full_name"
]
},
"middlename": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
},
"copy_to": [
"full_name"
]
},
"full_name": {
"type": "text",
"analyzer": "my_analyzer",
"fields": {
"keyword": {
"type": "keyword"
}
}
}
}
}
}
以下查询将返回预期的文档，但将 Leanne Ray 的分数高于 Anne Borg。
{
"query": {
"bool": {
"must": {
"query_string": {
"query": "Ann",
"fields": ["full_name"]
}
},
"should": {
"match": {
"full_name": "Ann"}
}
}
}
}
以下是结果...
"hits": [
{
"_index": "contacts_4",
"_type": "_doc",
"_id": "2",
"_score": 6.6333585,
"_source": {
"firstname": "Anne",
"middlename": "M",
"lastname": "Stone"
}
},
{
"_index": "contacts_4",
"_type": "_doc",
"_id": "1",
"_score": 6.142234,
"_source": {
"firstname": "Leanne",
"lastname": "Ray"
}
},
{
"_index": "contacts_4",
"_type": "_doc",
"_id": "3",
"_score": 6.079495,
"_source": {
"firstname": "Anne",
"lastname": "Borg"
}
}
同时使用 ngram 令牌过滤器和ngram 标记器似乎可以解决此问题......
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"filter": [
"ngram"
],
"tokenizer": "ngram"
}
}
}
},
"mappings": {
"properties": {
"firstname": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
},
"copy_to": [
"full_name"
]
},
"lastname": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
},
"copy_to": [
"full_name"
]
},
"middlename": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
},
"copy_to": [
"full_name"
]
},
"full_name": {
"type": "text",
"analyzer": "my_analyzer",
"search_analyzer": "my_analyzer"
}
}
}
}
同一查询会返回具有所需相对评分的预期结果。为什么会这样？请注意，上面，我使用的是带有小写过滤器的 ngram 分词器，这里唯一的区别是我使用的是 ngram 过滤器而不是小写过滤器。
以下是结果。请注意，Leanne Ray的得分低于Anne Borg和Anne M Stone，正如预期的那样。
"hits": [
{
"_index": "contacts_4",
"_type": "_doc",
"_id": "3",
"_score": 4.953257,
"_source": {
"firstname": "Anne",
"lastname": "Borg"
}
},
{
"_index": "contacts_4",
"_type": "_doc",
"_id": "2",
"_score": 4.87168,
"_source": {
"firstname": "Anne",
"middlename": "M",
"lastname": "Stone"
}
},
{
"_index": "contacts_4",
"_type": "_doc",
"_id": "1",
"_score": 1.0364896,
"_source": {
"firstname": "Leanne",
"lastname": "Ray"
}
}
顺便说一下，当索引还包含其他文档时，此查询还会返回大量误报结果。这不是一个问题，因为这些误报相对于所需命中的分数非常低。但仍然不理想。例如，如果我将 {firstname： Gideon， lastname： Grossma} 添加到文档中，则上述查询也会在结果集中带回该文档 - 尽管分数比包含字符串"Ann"的文档低得多
答案与链接线程中的答案相同。由于您正在对所有索引数据进行 ngraming，因此它与Ann的工作方式与Anne的工作方式相同，您将获得完全相同的响应(见下文(，但分数不同：
"hits" : [
{
"_index" : "test",
"_type" : "_doc",
"_id" : "5Jr-DHIBhYuDqANwSeiw",
"_score" : 4.8442974,
"_source" : {
"firstname" : "Anne",
"lastname" : "Borg"
}
},
{
"_index" : "test",
"_type" : "_doc",
"_id" : "5pr-DHIBhYuDqANwSeiw",
"_score" : 4.828779,
"_source" : {
"firstname" : "Anne",
"middlename" : "M",
"lastname" : "Stone"
}
},
{
"_index" : "test",
"_type" : "_doc",
"_id" : "5Zr-DHIBhYuDqANwSeiw",
"_score" : 0.12874341,
"_source" : {
"firstname" : "Leanne",
"lastname" : "Ray"
}
}
]
更新
这是一个修改后的查询，可用于检查部件(即annvsanne(。同样，大小写在这里没有区别，因为分析器在索引之前将所有内容都缩小了。
{
"query": {
"bool": {
"must": {
"query_string": {
"query": "ann",
"fields": [
"full_name"
]
}
},
"should": [
{
"match_phrase_prefix": {
"firstname": {
"query": "ann",
"boost": "10"
}
}
},
{
"match_phrase_prefix": {
"lastname": {
"query": "ann",
"boost": "10"
}
}
}
]
}
}
}