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"
}
最初，我使用的是ngram标记器。我的索引映射中还有一个生成的字段，称为“ full_name”，其中包含名字，中间名和姓氏字符串。当我搜索“ Anne”时，所有3个文档都在结果集中。但是，安妮·M·斯通的分数与莱安·雷相同。安妮·斯通（Ann M Stone）的得分应该比莱安（Leanne）高。
为了解决这个问题，我将我的ngram标记生成器更改为edge_ngram标记生成器。这具有将Leanne Ray从结果集中完全删除的效果。我们希望将此结果保留在结果集中-因为它仍然包含查询字符串-但得分比其他两个更好的匹配要低。
我读到某处可能在同一索引中将边缘ngram过滤器与ngram过滤器一起使用。如果是这样，我应该如何重新创建索引呢？有更好的解决方案吗？
这里是初始索引设置。
{
"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": "3",
"type": "ngram",
"max_gram": "4"
}
}
}
},
"mappings": {
"properties": {
"contact_id": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"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"
}
}
}
}
}
}
这是我的查询
{
"query": {
"bool": {
"should": [
{
"query_string": {
"query": "Anne",
"fields": [
"full_name"
]
}
}
]
}
}
}
这带来了这些结果
"hits": {
"total": {
"value": 3,
"relation": "eq"
},
"max_score": 0.59604377,
"hits": [
{
"_index": "contacts_15",
"_type": "_doc",
"_id": "3",
"_score": 0.59604377,
"_source": {
"firstname": "Anne",
"lastname": "Borg"
}
},
{
"_index": "contacts_15",
"_type": "_doc",
"_id": "1",
"_score": 0.5592099,
"_source": {
"firstname": "Anne",
"middlename": "M",
"lastname": "Stone"
}
},
{
"_index": "contacts_15",
"_type": "_doc",
"_id": "2",
"_score": 0.5592099,
"_source": {
"firstname": "Leanne",
"lastname": "Ray"
}
}
]
}
如果我改用边缘ngram标记器，这就是索引设置的样子...
{
"settings": {
"max_ngram_diff": "10",
"analysis": {
"analyzer": {
"my_analyzer": {
"filter": [
"lowercase"
],
"type": "custom",
"tokenizer": ["edge_ngram_tokenizer"]
}
},
"tokenizer": {
"edge_ngram_tokenizer": {
"token_chars": [
"letter",
"digit",
"custom"
],
"custom_token_chars": "'-",
"min_gram": "2",
"type": "edge_ngram",
"max_gram": "10"
}
}
}
},
"mappings": {
"properties": {
"contact_id": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"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"
}
}
}
}
}
}
并且同一查询带回了这个新结果集...
"hits": {
"total": {
"value": 2,
"relation": "eq"
},
"max_score": 1.5131824,
"hits": [
{
"_index": "contacts_16",
"_type": "_doc",
"_id": "3",
"_score": 1.5131824,
"_source": {
"firstname": "Anne",
"middlename": "M",
"lastname": "Stone"
}
},
{
"_index": "contacts_16",
"_type": "_doc",
"_id": "1",
"_score": 1.4100108,
"_source": {
"firstname": "Anne",
"lastname": "Borg"
}
}
]
}
您可以继续使用ngram（即第一个解决方案），但随后您需要更改查询以提高相关性。它的工作方式是在multi_match子句中添加增强的should查询，以增加其名字或姓氏与输入完全匹配的文档的分数：
{
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "Anne",
"fields": [
"full_name"
]
}
}
],
"should": [
{
"multi_match": {
"query": "Anne",
"fields": [
"firstname",
"lastname"
],
"boost": 10
}
}
]
}
}
}
此查询会将Anne Borg和Anne M Stone带到Leanne Ray之前。