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

[[query-dsl-mlt-query]]
=== More Like This Query

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

[source,js]
--------------------------------------------------
{
    "more_like_this" : {
        "fields" : ["name.first", "name.last"],
        "like_text" : "text like this one",
        "min_term_freq" : 1,
        "max_query_terms" : 12
    }
}
--------------------------------------------------

More Like This can find documents that are "like" a set of
chosen documents. The syntax to specify one or more documents is similar to
the <<docs-multi-get,Multi GET API>>, and supports the `ids` or `docs` array.
If only one document is specified, the query behaves the same as the 
<<search-more-like-this,More Like This API>>.

[source,js]
--------------------------------------------------
{
    "more_like_this" : {
        "fields" : ["name.first", "name.last"],
        "docs" : [
        {
            "_index" : "test",
            "_type" : "type",
            "_id" : "1"
        },
        {
            "_index" : "test",
            "_type" : "type",
            "_id" : "2"
        }
        ],
        "ids" : ["3", "4"],
        "min_term_freq" : 1,
        "max_query_terms" : 12
    }
}
--------------------------------------------------

Additionally, the `doc` syntax of the
<<docs-multi-termvectors,Multi Term Vectors API>> is also supported. This is useful in
order to specify one or more documents not present in the index, and in
this case should be preferred over only using `like_text`.

[source,js]
--------------------------------------------------
{
    "more_like_this" : {
        "fields" : ["name.first", "name.last"],
        "docs" : [
        {
            "_index" : "test",
            "_type" : "type",
            "doc" : {
                "name": {
                    "first": "Ben",
                    "last": "Grimm"
                },
                "tweet": "You got no idea what I'd... what I'd give to be invisible."
              }
            }
        },
        {
            "_index" : "test",
            "_type" : "type",
            "_id" : "2"
        }
        ],
        "min_term_freq" : 1,
        "max_query_terms" : 12
    }
}
--------------------------------------------------

`more_like_this` can be shortened to `mlt`.

Under the hood, `more_like_this` simply creates multiple `should` clauses in a `bool` query of
interesting terms extracted from some provided text. The interesting terms are
selected with respect to their tf-idf scores. These are controlled by
`min_term_freq`, `min_doc_freq`, and `max_doc_freq`. The number of interesting
terms is controlled by `max_query_terms`. While the minimum number of clauses
that must be satisfied is controlled by `percent_terms_to_match`. The terms
are extracted from `like_text` which is analyzed by the analyzer associated
with the field, unless specified by `analyzer`. There are other parameters,
such as `min_word_length`, `max_word_length` or `stop_words`, to control what
terms should be considered as interesting. In order to give more weight to
more interesting terms, each boolean clause associated with a term could be
boosted by the term tf-idf score times some boosting factor `boost_terms`.
When a search for multiple `docs` is issued, More Like This generates a
`more_like_this` query per document field in `fields`. These `fields` are
specified as a top level parameter or within each `doc`.

IMPORTANT: The fields must be indexed and of type `string`. Additionally, when
using `ids` or `docs`, the fields must be either `stored`, store `term_vector`
or `_source` must be enabled.

The `more_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 for `like_text` and to all possible fields
for `ids` or `docs`.

|`like_text` |The text to find documents like it, *required* if `ids` or `docs` are
not specified.

|`ids` or `docs` |A list of documents following the same syntax as the 
<<docs-multi-get,Multi GET API>> or <<docs-multi-termvectors,Multi Term Vectors API>>.
The text is fetched from `fields` unless specified otherwise in each `doc`.

|`include` |When using `ids` or `docs`, specifies whether the documents should be
included from the search. Defaults to `false`.

|`minimum_should_match`| From the generated query, the number of terms that
must match following the <<query-dsl-minimum-should-match,minimum should
syntax>>. (Defaults to `"30%"`).

|`min_term_freq` |The frequency below which terms will be ignored in the
source doc. The default frequency is `2`.

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

|`stop_words` |An array of stop words. Any word in this set is
considered "uninteresting" and ignored. Even if your Analyzer allows
stopwords, you might want to tell the MoreLikeThis code to ignore them,
as for the purposes of document similarity it seems reasonable to assume
that "a stop word is never interesting".

|`min_doc_freq` |The frequency at which words will be ignored which do
not occur in at least this many docs. Defaults to `5`.

|`max_doc_freq` |The maximum frequency in which words may still appear.
Words that appear in more than this many docs will be ignored. Defaults
to unbounded.

|`min_word_length` |The minimum word length below which words will be
ignored. Defaults to `0`.(Old name "min_word_len" is deprecated)

|`max_word_length` |The maximum word length above which words will be
ignored. Defaults to unbounded (`0`). (Old name "max_word_len" is deprecated)

|`boost_terms` |Sets the boost factor to use when boosting terms.
Defaults to deactivated (`0`). Any other value activates boosting with given
boost factor.

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

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