OpenSearch/docs/reference/query-dsl/queries/flt-query.asciidoc

[[query-dsl-flt-query]]
=== Fuzzy Like This Query

Fuzzy like this query find documents that are "like" provided text by
running it against one or more fields.

[source,js]
--------------------------------------------------
{
    "fuzzy_like_this" : {
        "fields" : ["name.first", "name.last"],
        "like_text" : "text like this one",
        "max_query_terms" : 12
    }
}
--------------------------------------------------

`fuzzy_like_this` can be shortened to `flt`.

The `fuzzy_like_this` top level parameters include:

[cols="<,<",options="header",]
|=======================================================================
|Parameter |Description
|`fields` |A list of the fields to run the more like this query against.
Defaults to the `_all` field.

|`like_text` |The text to find documents like it, *required*.

|`ignore_tf` |Should term frequency be ignored. Defaults to `false`.

|`max_query_terms` |The maximum number of query terms that will be
included in any generated query. Defaults to `25`.

|`fuzziness` |The minimum similarity of the term variants. Defaults
to `0.5`. See  <<fuzziness>>.

|`prefix_length` |Length of required common prefix on variant terms.
Defaults to `0`.

|`boost` |Sets the boost value of the query. Defaults to `1.0`.

|`analyzer` |The analyzer that will be used to analyze the text.
Defaults to the analyzer associated with the field.
|=======================================================================

[float]
==== How it Works

Fuzzifies ALL terms provided as strings and then picks the best n
differentiating terms. In effect this mixes the behaviour of FuzzyQuery
and MoreLikeThis but with special consideration of fuzzy scoring
factors. This generally produces good results for queries where users
may provide details in a number of fields and have no knowledge of
boolean query syntax and also want a degree of fuzzy matching and a fast
query.

For each source term the fuzzy variants are held in a BooleanQuery with
no coord factor (because we are not looking for matches on multiple
variants in any one doc). Additionally, a specialized TermQuery is used
for variants and does not use that variant term's IDF because this would
favor rarer terms, such as misspellings. Instead, all variants use the
same IDF ranking (the one for the source query term) and this is
factored into the variant's boost. If the source query term does not
exist in the index the average IDF of the variants is used.