# Custom score for fuzzy matching based on Levenshtein distance score

**URL:** <https://discuss.elastic.co/t/custom-score-for-fuzzy-matching-based-on-levenshtein-distance-score/125544>\
**Category:** Elasticsearch\
**Created:** [March 26, 2018, 5:43am UTC](https://discuss.elastic.co/t/custom-score-for-fuzzy-matching-based-on-levenshtein-distance-score/125544 "2018-03-26T05:43:37Z")\
**Posts on this page:** 1\
**Showing post:** 2

<div class="post-metadata">

**Author:** ![abdon](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/abdon/32/9195_2.png) [@abdon](https://discuss.elastic.co/u/abdon)\
**Post date:** [March 28, 2018, 8:59am UTC](https://discuss.elastic.co/t/custom-score-for-fuzzy-matching-based-on-levenshtein-distance-score/125544/2 "2018-03-28T08:59:45Z")

</div>

The [match query accepts a `fuzziness` parameter](https://www.elastic.co/guide/en/elasticsearch/guide/current/fuzzy-match-query.html) which allows you to do fuzzy matching based on the Damerau-Levenshtein edit distance (see the explanation for the fuzziness parameter in our docs [here](https://www.elastic.co/guide/en/elasticsearch/guide/current/fuzziness.html)).

Now, two things to keep in mind with `fuzziness`: the maximum edit distance that Elasticsearch supports is 2, so `Smith` and `SSmithhh` will never be a match. And, the default scoring is not quite how you want it to be.

However, if a maximum edit distance of 2 is enough for your use case, then you can use a combination of `bool` and `constant_score` queries to get to the scoring you want. The idea is that we look for all documents that match with an edit distance of 2, and assign a score of 80 to those documents. Then, if those documents also match with an edit distance of 1, increase the score by 10 points. And if those documents also match with an edit distance of 0 - assign another 10 points to get to a perfect score of 100.

So, let's say these are your documents:

```auto
PUT levtest/_doc/_bulk
{ "index" : { "_id": 1 } }
{ "name": "Smith" }
{ "index" : { "_id": 2 } }
{ "name": "Smithh" }
{ "index" : { "_id": 3 } }
{ "name": "SSmithh" }

```

Then your query could look like this:

```auto
GET levtest/_search
{
  "query": {
    "bool": {
      "should": [
        {
          "constant_score": {
            "filter": {
              "match": {
                "name": {
                  "query": "Smith",
                  "fuzziness": 2
                }
              }
            },
            "boost": 80
          }
        },
        {
          "constant_score": {
            "filter": {
              "match": {
                "name": {
                  "query": "Smith",
                  "fuzziness": 1
                }
              }
            },
            "boost": 10
          }
        },
        {
          "constant_score": {
            "filter": {
              "match": {
                "name": {
                  "query": "Smith"
                }
              }
            },
            "boost": 10
          }
        }
      ]
    }
  }
}

```

Returning:

```auto
    "hits": [
      {
        "_index": "levtest",
        "_type": "_doc",
        "_id": "1",
        "_score": 100,
        "_source": {
          "name": "Smith"
        }
      },
      {
        "_index": "levtest",
        "_type": "_doc",
        "_id": "2",
        "_score": 90,
        "_source": {
          "name": "Smithh"
        }
      },
      {
        "_index": "levtest",
        "_type": "_doc",
        "_id": "3",
        "_score": 80,
        "_source": {
          "name": "SSmithh"
        }
      }
    ]

```

A warning though: queries with a `"fuzziness": 2` are computationally expensive.

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_[View the full topic](https://discuss.elastic.co/t/custom-score-for-fuzzy-matching-based-on-levenshtein-distance-score/125544)._