如何在 Elasticsearch 7 中使用 ngram 分词器找出大写单词

Question

我必须用 temp 或温度

这是我的 ngram 分词器索引和一些示例文档

# index
PUT /demo
{
    "settings": {
        "index": {
            "max_ngram_diff": "20",
            "analysis": {
                "analyzer": {
                    "account_analyzer": {
                        "tokenizer": "account_tokenizer"
                    }
                },
                "tokenizer": {
                    "account_tokenizer": {
                        "token_chars": [
                            "letter",
                            "digit"
                        ],
                        "min_gram": "1",
                        "type": "ngram",
                        "max_gram": "15"
                    }
                }
            }
        }
    },
    "mappings": {
        "properties": {
            "account": {
                "type": "text",
                "fields": {
                    "keyword": {
                        "type": "keyword",
                        "ignore_above": 256
                    }
                },
                "analyzer": "account_analyzer",
                "search_analyzer": "standard"
            }
        }
    }
}

# docs

PUT /demo/_doc/1
{
  "account": "temp123"
}

PUT /demo/_doc/2
{
  "account": "TEMP456"
}

通过以下查询，我希望能取回这两个文档。但我只有 doc 1。好像我无法获得带有大写单词的文档。

我应该如何使用 temp 或 TEMP 取回两个文档？

POST /demo/_search/
{
  "query": {
      "bool": {
          "must": [
            {
              "match": {
                "account": {
                  "query": "temp",
                  "fuzziness": "AUTO"
                }
              }
             }
          ]
      }
  }
}


POST /demo/_search/
{
  "query": {
      "bool": {
          "must": [
            {
              "match": {
                "account": {
                  "query": "TEMP",
                  "fuzziness": "AUTO"
                }
              }
             }
          ]
      }
  }
}

Answer 1

您可以使用 _analyze 检查分析器生成的标记。

GET demo/_analyze
{
  "analyzer": "account_analyzer",
  "text": ["TEMP123"]
}

"tokens" : [
    {
      "token" : "T",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "TE",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "TEM",
      "start_offset" : 0,
      "end_offset" : 3,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "TEMP",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "TEMP1",
      "start_offset" : 0,
      "end_offset" : 5,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "TEMP12",
      "start_offset" : 0,
      "end_offset" : 6,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "TEMP123",
      "start_offset" : 0,
      "end_offset" : 7,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : "E",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "EM",
      "start_offset" : 1,
      "end_offset" : 3,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : "EMP",
      "start_offset" : 1,
      "end_offset" : 4,
      "type" : "word",
      "position" : 9
    },
    {
      "token" : "EMP1",
      "start_offset" : 1,
      "end_offset" : 5,
      "type" : "word",
      "position" : 10
    },
    {
      "token" : "EMP12",
      "start_offset" : 1,
      "end_offset" : 6,
      "type" : "word",
      "position" : 11
    },
    {
      "token" : "EMP123",
      "start_offset" : 1,
      "end_offset" : 7,
      "type" : "word",
      "position" : 12
    },
    {
      "token" : "M",
      "start_offset" : 2,
      "end_offset" : 3,
      "type" : "word",
      "position" : 13
    },
    {
      "token" : "MP",
      "start_offset" : 2,
      "end_offset" : 4,
      "type" : "word",
      "position" : 14
    },
    {
      "token" : "MP1",
      "start_offset" : 2,
      "end_offset" : 5,
      "type" : "word",
      "position" : 15
    },
    {
      "token" : "MP12",
      "start_offset" : 2,
      "end_offset" : 6,
      "type" : "word",
      "position" : 16
    },
    {
      "token" : "MP123",
      "start_offset" : 2,
      "end_offset" : 7,
      "type" : "word",
      "position" : 17
    },
    {
      "token" : "P",
      "start_offset" : 3,
      "end_offset" : 4,
      "type" : "word",
      "position" : 18
    },
    {
      "token" : "P1",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "word",
      "position" : 19
    },
    {
      "token" : "P12",
      "start_offset" : 3,
      "end_offset" : 6,
      "type" : "word",
      "position" : 20
    },
    {
      "token" : "P123",
      "start_offset" : 3,
      "end_offset" : 7,
      "type" : "word",
      "position" : 21
    },
    {
      "token" : "1",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "word",
      "position" : 22
    },
    {
      "token" : "12",
      "start_offset" : 4,
      "end_offset" : 6,
      "type" : "word",
      "position" : 23
    },
    {
      "token" : "123",
      "start_offset" : 4,
      "end_offset" : 7,
      "type" : "word",
      "position" : 24
    },
    {
      "token" : "2",
      "start_offset" : 5,
      "end_offset" : 6,
      "type" : "word",
      "position" : 25
    },
    {
      "token" : "23",
      "start_offset" : 5,
      "end_offset" : 7,
      "type" : "word",
      "position" : 26
    },
    {
      "token" : "3",
      "start_offset" : 6,
      "end_offset" : 7,
      "type" : "word",
      "position" : 27
    }
  ]

您需要向分析器添加一个小写过滤器，以便生成的所有标记都具有小写

{
  "settings": {
    "index": {
      "max_ngram_diff": "20",
      "analysis": {
        "analyzer": {
          "account_analyzer": {
            "tokenizer": "account_tokenizer",
            "filter": [                       ----> note
              "lowercase"
            ]
          }
        },
        "tokenizer": {
          "account_tokenizer": {
            "token_chars": [
              "letter",
              "digit"
            ],
            "min_gram": "1",
            "type": "ngram",
            "max_gram": "15"
          }
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "account": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        },
        "analyzer": "account_analyzer",
        "search_analyzer": "standard"
      }
    }
  }
}

如何在 Elasticsearch 7 中使用 ngram 分词器找出大写单词

How to find out capital word with ngram tokenizer in Elasticsearch 7

elasticsearch

elasticsearch-7