Elasticsearch 多字段模糊搜索不首先返回完全匹配

Question

我正在对“文本”和“关键字”字段执行模糊弹性搜索查询。我在 elasticsearch 中有两个文档，一个是“text”“testPhone 5”，另一个是“testPhone 4s”。当我使用“testPhone 5”执行模糊查询时，我看到两个文档都被赋予了完全相同的分数值。为什么会出现这种情况？

额外信息：我正在使用“uax_url_email”标记器和“小写”过滤器为文档编制索引。

这是我正在做的查询：

{
    query : {
        bool: {
            // match one or the other fuzzy query
            should: [
                {
                    fuzzy: {
                        text: {
                            min_similarity: 0.4,
                            value: 'testphone 5',
                            prefix_length: 0,
                            boost: 5,
                        }
                    }
                },
                {
                    fuzzy: {
                        keywords: {
                            min_similarity: 0.4,
                            value: 'testphone 5',
                            prefix_length: 0,
                            boost: 1,
                        }
                    }
                }
            ]
        }
    },
    sort: [ 
        '_score'
    ],
    explain: true
}

这是结果：

{ max_score: 0.47213298,
  total: 2,
  hits:
  [ { _index: 'test',
     _shard: 0,
     _id: '51fbf95f82e89ae8c300002c',
     _node: '0Mtfzbe1RDinU71Ordx-Ag',
     _source:
    { next: { id: '51fbf95f82e89ae8c3000027' },
      cards: [ '51fbf95f82e89ae8c3000027', [length]: 1 ],
      other: false,
      _id: '51fbf95f82e89ae8c300002c',
      category: '51fbf95f82e89ae8c300002b',
      image: 'https://s3.amazonaws.com/sold_category_icons/Smartphones.png',
      text: 'testPhone 5',
      keywords: [ [length]: 0 ],
      __v: 0 },
   _type: 'productgroup',
   _explanation:
    { details:
       [ { details:
            [ { details:
                 [ { details:
                      [ { details:
                           [ { value: 3.8888888, description: 'boost' },
                             { value: 1.5108256,
                               description: 'idf(docFreq=2, maxDocs=5)' },
                             { value: 0.17020021,
                               description: 'queryNorm' },
                             [length]: 3 ],
                          value: 0.99999994,
                          description: 'queryWeight, product of:' },
                        { details:
                           [ { details:
                                [ { value: 1, description: 'termFreq=1.0' },
                                  [length]: 1 ],
                               value: 1,
                               description: 'tf(freq=1.0), with freq of:' },
                             { value: 1.5108256,
                               description: 'idf(docFreq=2, maxDocs=5)' },
                             { value: 0.625,
                               description: 'fieldNorm(doc=0)' },
                             [length]: 3 ],
                          value: 0.944266,
                          description: 'fieldWeight in 0, product of:' },
                        [length]: 2 ],
                     value: 0.94426596,
                     description: 'score(doc=0,freq=1.0 = termFreq=1.0\n), product of:' },
                   [length]: 1 ],
                value: 0.94426596,
                description: 'weight(text:testphone^3.8888888 in 0) [PerFieldSimilarity], result of:' },
              [length]: 1 ],
           value: 0.94426596,
           description: 'sum of:' },
         { value: 0.5, description: 'coord(1/2)' },
         [length]: 2 ],
      value: 0.47213298,
      description: 'product of:' },
   _score: 0.47213298 },
 { _index: 'test',
   _shard: 4,
   _id: '51fbf95f82e89ae8c300002d',
   _node: '0Mtfzbe1RDinU71Ordx-Ag',
   _source:
    { next: { id: '51fbf95f82e89ae8c3000027' },
      cards: [ '51fbf95f82e89ae8c3000029', [length]: 1 ],
      other: false,
      _id: '51fbf95f82e89ae8c300002d',
      category: '51fbf95f82e89ae8c300002b',
      image: 'https://s3.amazonaws.com/sold_category_icons/Smartphones.png',
      text: 'testPhone 4s',
      keywords: [ 'apple', [length]: 1 ],
      __v: 0 },
   _type: 'productgroup',
   _explanation:
    { details:
       [ { details:
            [ { details:
                 [ { details:
                      [ { details:
                           [ { value: 3.8888888, description: 'boost' },
                             { value: 1.5108256,
                               description: 'idf(docFreq=2, maxDocs=5)' },
                             { value: 0.17020021,
                               description: 'queryNorm' },
                             [length]: 3 ],
                          value: 0.99999994,
                          description: 'queryWeight, product of:' },
                        { details:
                           [ { details:
                                [ { value: 1, description: 'termFreq=1.0' },
                                  [length]: 1 ],
                               value: 1,
                               description: 'tf(freq=1.0), with freq of:' },
                             { value: 1.5108256,
                               description: 'idf(docFreq=2, maxDocs=5)' },
                             { value: 0.625,
                               description: 'fieldNorm(doc=0)' },
                             [length]: 3 ],
                          value: 0.944266,
                          description: 'fieldWeight in 0, product of:' },
                        [length]: 2 ],
                     value: 0.94426596,
                     description: 'score(doc=0,freq=1.0 = termFreq=1.0\n), product of:' },
                   [length]: 1 ],
                value: 0.94426596,
                description: 'weight(text:testphone^3.8888888 in 0) [PerFieldSimilarity], result of:' },
              [length]: 1 ],
           value: 0.94426596,
           description: 'sum of:' },
         { value: 0.5, description: 'coord(1/2)' },
         [length]: 2 ],
      value: 0.47213298,
      description: 'product of:' },
   _score: 0.47213298 },
 [length]: 2 ] }

Answer 1

我最近自己也遇到了这个问题。我无法确切地告诉你为什么会发生这种情况，但我可以告诉你我是如何修复它的：

我对同一字段运行了 2 个查询，其中一个查询完全匹配，然后在同一字段上运行了完全相同的查询，启用了模糊匹配且提升较低。

这确保了我的精确匹配总是比模糊匹配结束得更高。

PS我认为他们的得分相等，因为由于模糊性，两者匹配并且ES并不关心其中一个是否完全匹配，只要两者匹配即可，但这纯粹是我的理论，因为我对评分算法不太熟悉。