当我如下查询模糊匹配时，elasticsearch仍然仅返回_score。但我期望基于模糊算法的匹配百分比。我以为这是一件简单的可配置的事情，但我找不到它，因为显示模糊匹配结果的匹配百分比很常见。
怎么做？还是在弹性搜索中这不是常见的“实践”？但是我在大多数用户界面中发现的确显示了模糊匹配的匹配百分比得分。
"query": {
"fuzzy": {
"name": {
"value": "Shahid"
}
}
}
响应：
"hits" : [{
"_index" : "users",
"_type" : "user",
"_id" : "5sadsadsaddas",
"_score" : 0.11127616,
"fuzzyMatchPercentage": 100% // I expect something like this here
"_source" : {
"name" : "Shahid",
"email" : "shahid@codeforgeek.com",
"city" : "mumbai"
}
},
答案 0 :(得分：0)
如评论中所述，fuzzy查询在Elasticsearch中的工作方式并非如此。默认情况下，搜索结果按降序排序，其中分数表示文档与特定查询的匹配程度。模糊性方面包含在该分数的计算中：查询匹配的越精确/越模糊，分数就越高。您可以通过请求详细的分数说明来验证这一点（在Elasticsearch v7.x中，模糊性方面已包含在提升因子的计算中）。看下面的例子：
1。索引两个示例文档（一个具有专有名称，一个具有名称拼写错误）
POST fuzzy/_bulk
{"index":{"_id":1}}
{"name": "Shahid"}
{"index":{"_id":2}}
{"name": "Shahib"}
2。用fuzzy-查询搜索名称“ Shahid”
GET fuzzy/_search
{
"explain": true,
"query": {
"fuzzy": {
"name": {
"value": "Shahid"
}
}
}
}
3。两个匹配文档的得分和解释子句
对于拼写正确的文档（“ Shahid”）：
"_explanation" : {
"value" : 0.57762265,
"description" : "sum of:",
"details" : [
{
"value" : 0.57762265,
"description" : "weight(name:shahid in 0) [PerFieldSimilarity], result of:",
"details" : [
{
"value" : 0.57762265,
"description" : "score(freq=1.0), product of:",
"details" : [
{
"value" : 1.8333334,
"description" : "boost",
"details" : [ ]
},
{
"value" : 0.6931472,
"description" : "idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:",
"details" : [
{
"value" : 1,
"description" : "n, number of documents containing term",
"details" : [ ]
},
{
"value" : 2,
"description" : "N, total number of documents with field",
"details" : [ ]
}
]
},
{
"value" : 0.45454544,
"description" : "tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:",
"details" : [
{
"value" : 1.0,
"description" : "freq, occurrences of term within document",
"details" : [ ]
},
{
"value" : 1.2,
"description" : "k1, term saturation parameter",
"details" : [ ]
},
{
"value" : 0.75,
"description" : "b, length normalization parameter",
"details" : [ ]
},
{
"value" : 1.0,
"description" : "dl, length of field",
"details" : [ ]
},
{
"value" : 1.0,
"description" : "avgdl, average length of field",
"details" : [ ]
}
]
}
]
}
]
}
]
}
对于拼写错误的文档（“ Shahib”）：
"_explanation" : {
"value" : 0.46209806,
"description" : "sum of:",
"details" : [
{
"value" : 0.46209806,
"description" : "weight(name:shahib in 1) [PerFieldSimilarity], result of:",
"details" : [
{
"value" : 0.46209806,
"description" : "score(freq=1.0), product of:",
"details" : [
{
"value" : 1.4666666,
"description" : "boost",
"details" : [ ]
},
{
"value" : 0.6931472,
"description" : "idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:",
"details" : [
{
"value" : 1,
"description" : "n, number of documents containing term",
"details" : [ ]
},
{
"value" : 2,
"description" : "N, total number of documents with field",
"details" : [ ]
}
]
},
{
"value" : 0.45454544,
"description" : "tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:",
"details" : [
{
"value" : 1.0,
"description" : "freq, occurrences of term within document",
"details" : [ ]
},
{
"value" : 1.2,
"description" : "k1, term saturation parameter",
"details" : [ ]
},
{
"value" : 0.75,
"description" : "b, length normalization parameter",
"details" : [ ]
},
{
"value" : 1.0,
"description" : "dl, length of field",
"details" : [ ]
},
{
"value" : 1.0,
"description" : "avgdl, average length of field",
"details" : [ ]
}
]
}
]
}
]
}
]
}
4。结论 不幸的是，没有关于升压因子的详细解释（Elasticsearch问题），但是从示例中可以看出，这是对两个文档评分的唯一区别：