如何使用 Elasticsearch 对文本输入执行部分单词搜索? [英] How to perform partial word searches on a text input with Elasticsearch?

问题描述

我有一个查询以下列格式搜索记录:TR000002_1_2020.

I have a query to search for records in the following format: TR000002_1_2020.

用户应该能够通过以下方式搜索结果:

Users should be able to search for results the following ways:

TR000002 或 2_1_2020 或 TR000002_1_2020 或 2020.我使用的是 Elasticsearch 6.8，所以我不能使用 E7 中引入的内置 Search-As-You-Type.因此，我认为 wildcard 搜索或 ngram 可能最适合我的需要.以下是我的两种方法以及它们不起作用的原因.

TR000002 or 2_1_2020 or TR000002_1_2020 or 2020. I am using Elasticsearch 6.8 so I cannot use the built in Search-As-You-Type introduced in E7. Thus, I figured either wildcard searches or ngram may best suit what I needed. Here were my two approaches and why they did not work.

1. 通配符

属性映射:

.Text(t => t
    .Name(tr => tr.TestRecordId)
)

查询:

m => m.Wildcard(w => w
    .Field(tr => tr.TestRecordId)
    .Value($"*{form.TestRecordId}*")
),

这有效，但它区分大小写，因此如果用户使用 tr000002_1_2020 进行搜索，则不会返回任何结果(因为 t 和 r> 在查询中是小写的)

This works but it is case-sensitive so if the user searches with tr000002_1_2020, then no results would return (since the t and r are lowercased in the query)

1. ngram(在输入等效项时搜索)

创建自定义 ngram 分析器

Create a custom ngram analyzer

.Analysis(a => a
    .Analyzers(aa => aa
        .Custom("autocomplete", ca => ca
            .Tokenizer("autocomplete")
            .Filters(new string[] {
                "lowercase"
            })
        )
        .Custom("autocomplete_search", ca => ca
            .Tokenizer("lowercase")
        )
    )
    .Tokenizers(t => t
        .NGram("autocomplete", e => e
            .MinGram(2)
            .MaxGram(16)
            .TokenChars(new TokenChar[] {
                TokenChar.Letter,
                TokenChar.Digit,
                TokenChar.Punctuation,
                TokenChar.Symbol
            })
        )
    )
)

属性映射

.Text(t => t
    .Name(tr => tr.TestRecordId)
    .Analyzer("autocomplete")
    .SearchAnalyzer("autocomplete_search")
)

查询

m => m.Match(m => m
    .Query(form.TestRecordId)
),

如在本答案中所述，这不起作用，因为标记器将字符拆分为像 20 和 02 和 2020，因此我的查询返回了索引中包含 2020 的所有文档，例如 TR000002_1_2020和 TR000008_1_2020 和 TR000003_6_2020.

As described in this answer, this does not work since the tokenizer splits the characters up in to elements like 20 and 02 and 2020, so as a result my queries returned all documents in my index that contained 2020 such as TR000002_1_2020 and TR000008_1_2020 and TR000003_6_2020.

如何最好地利用 Elasticsearch 来实现我想要的搜索行为?我也看到了 query string 的使用.谢谢！

What's the best utilization of Elasticsearch to allow my desired search behavior? I've seen query string used as well. Thanks!

推荐答案

这里有一个简单的方法来满足您的要求(我希望如此).

here is a simple way to address your requirements ( I hope ).

1. 我们使用模式替换字符过滤器来移除引用的固定部分 (TR000...)
2. 我们使用拆分标记器来拆分_"上的引用.字符
3. 我们使用 matchPhrase 查询来确保引用的片段按顺序匹配

使用此分析链作为参考 TR000002_1_2020 我们得到标记 [2", 1", 2020";].所以它会匹配查询[TR000002_1_2020"、TR000002 1 2020"、2_1_2020"、1_2020"]，但它不会匹配3_1_20或 2_2_2020.

with this analysis chain for reference TR000002_1_2020 we get the tokens ["2", "1", "2020" ]. So it will matches the queries ["TR000002_1_2020", "TR000002 1 2020", "2_1_2020", "1_2020"], but it will not match 3_1_2020 or 2_2_2020.

这是一个映射和分析的例子.它不在 Nest 中，但我认为您可以进行翻译.

Here is an example of mapping and analysis. It's not in Nest but I think you will be able to make the translation.

PUT pattern_split_demo
{
  "settings": {
    "analysis": {
      "char_filter": {
        "replace_char_filter": {
          "type": "pattern_replace",
          "pattern": "^TR0*",
          "replacement": ""
        }
      },
      "tokenizer": {
        "split_tokenizer": {
          "type": "simple_pattern_split",
          "pattern": "_"
        }
      },
      "analyzer": {
        "split_analyzer": {
          "tokenizer": "split_tokenizer",
          "filter": [
            "lowercase"
          ],
          "char_filter": [
            "replace_char_filter"
          ]
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "content": {
        "type": "text",
        "analyzer": "split_analyzer"
      }
    }
  }
}

POST pattern_split_demo/_analyze
{
  "text": "TR000002_1_2020",
  "analyzer": "split_analyzer"
} => ["2", "1", "2020"]

POST pattern_split_demo/_doc?refresh=true
{
  "content": "TR000002_1_2020"
}

POST pattern_split_demo/_search
{
  "query": {
    "match_phrase": {
      "content": "TR000002_1_2020"
    }
  }
}

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