ES建议，搜索索引项中的所有单词不仅是第一个单词

如何解决ES建议，搜索索引项中的所有单词不仅是第一个单词

基于this答案（第一个选项），我创建了该索引：

    'settings' => array(
        'analysis' => array(
            'analyzer' => array(
                'stop_analyzer' => array( 
                    'type' => 'custom','tokenizer' => 'standard','filter' => array(
                        'lowercase','english_stop'
                    )
                )
            ),"filter" => array(
                "english_stop" => array(
                    "type" => "stop","stopwords" => "_english_"
                )
            )
        )
    ),'mappings' => array(
        'properties' => array(
            'texts' => array(
                'type' => 'completion',"analyzer" => "stop_analyzer","search_analyzer" => "stop_analyzer",'preserve_position_increments' => false
            ),),)

当我开始建议搜索时（带或不带停用词），此功能非常理想。但是，例如，当我在索引This is the text中有此内容，并且搜索text时，我不会得到任何结果，那么执行此操作的正确方法是什么？我宁愿不使用N-gram。

我的搜索查询：

'suggest' => array(
    'suggestion' => array(
        'prefix'=> 'text','completion' => array(
            'field' => 'texts'
        )
    )
)

解决方法

根据用户给出的注释，添加另一个答案，以便使用n-gram搜索所有单词。以前的方法效果很好，但是使用正则表达式非常昂贵。

添加带有索引映射，索引数据，搜索查询和搜索结果的工作示例

索引映射：

{
  "settings": {
    "analysis": {
      "filter": {
        "my_custom_stop_words_filter": {
          "type": "stop","ignore_case": true,"stopwords": [
            "and","is","the"
          ]
        },"ngram_filter": {
          "type": "ngram","min_gram": 4,"max_gram": 20
        }
      },"analyzer": {
        "ngram_analyzer": {
          "type": "custom","tokenizer": "standard","filter": [
            "lowercase","ngram_filter","my_custom_stop_words_filter"
          ]
        }
      }
    },"max_ngram_diff": 50
  },"mappings": {
    "properties": {
      "title": {
        "type": "text","analyzer": "ngram_analyzer","search_analyzer": "standard"
      }
    }
  }
}

分析API

POST/_analyze
{
  "analyzer" : "ngram_analyzer","text" : "This is the text"
}

会生成以下令牌：

{
    "tokens": [
        {
            "token": "this","start_offset": 0,"end_offset": 4,"type": "<ALPHANUM>","position": 0
        },{
            "token": "text","start_offset": 12,"end_offset": 16,"position": 3
        }
    ]
}

索引数据：

{
  "title": [
    "This is the text"
  ]
}

搜索查询：

{
    "query": {
        "match": {
           "title": "text"
        }
    }
}

搜索结果：

"hits": [
            {
                "_index": "stof_29753971","_type": "_doc","_id": "1","_score": 0.41978103,"_source": {
                    "title": [
                        "This is the text"
                    ]
                }
            }
        ]

,

完成建议的最佳方式，可以与的中间匹配 字段是n-gram过滤器。

但是，由于您不想使用n-gram，因此可以尝试以下方法：

您可以使用多个建议，其中一个建议基于前缀，对于字段中间的匹配，可以使用正则表达式。

添加具有索引映射，数据，搜索查询和搜索结果的工作示例

索引映射：

{
  "settings": {
    "analysis": {
      "filter": {
        "my_custom_stop_words_filter": {
          "type": "stop","stopwords": [ "and","the" ]
        }
      },"analyzer": {
        "autocomplete": {
          "type": "custom","tokenizer": "whitespace","my_custom_stop_words_filter"
          ]
        }
      }
    }
  },"mappings": {
    "properties": {
      "title": {
        "type": "keyword"
      },"suggest": {
        "type": "completion","analyzer": "autocomplete","search_analyzer": "standard"
      }
    }
  }
}

索引数据：

{
  "suggest": [
    {
      "input": "This is the text"
    }
  ]
}
{
  "suggest": [
    {
      "input": "Software Manager"
    }
  ]
}

搜索查询：

{
    "suggest": {
        "suggest-exact": {
            "prefix": "text","completion": {
                "field": "suggest","skip_duplicates": true
            }
        },"suggest-regex": {
            "regex": ".*text.*","skip_duplicates": true
            }
        }
    }
}

搜索结果：

"suggest": {
    "suggest-exact": [
      {
        "text": "text","offset": 0,"length": 4,"options": []
      }
    ],"suggest-regex": [
      {
        "text": ".*text.*","length": 8,"options": [
          {
            "text": "This is the text","_index": "test","_score": 1.0,"_source": {
              "suggest": [
                {
                  "input": "This is the text"
                }
              ]
            }
          }
        ]
      }
    ]
  }