OpenSearch/docs/reference/analysis/tokenizers/ngram-tokenizer.asciidoc

[[analysis-ngram-tokenizer]]
=== NGram Tokenizer

The `ngram` tokenizer first breaks text down into words whenever it encounters
one of a list of specified characters, then it emits
https://en.wikipedia.org/wiki/N-gram[N-grams] of each word of the specified
length.

N-grams are like a sliding window that moves across the word - a continuous
sequence of characters of the specified length. They are useful for querying
languages that don't use spaces or that have long compound words, like German.

[float]
=== Example output

With the default settings, the `ngram` tokenizer treats the initial text as a
single token and produces N-grams with minimum length `1` and maximum length
`2`:

[source,js]
---------------------------
POST _analyze
{
  "tokenizer": "ngram",
  "text": "Quick Fox"
}
---------------------------
// CONSOLE

/////////////////////

[source,js]
----------------------------
{
  "tokens": [
    {
      "token": "Q",
      "start_offset": 0,
      "end_offset": 1,
      "type": "word",
      "position": 0
    },
    {
      "token": "Qu",
      "start_offset": 0,
      "end_offset": 2,
      "type": "word",
      "position": 1
    },
    {
      "token": "u",
      "start_offset": 1,
      "end_offset": 2,
      "type": "word",
      "position": 2
    },
    {
      "token": "ui",
      "start_offset": 1,
      "end_offset": 3,
      "type": "word",
      "position": 3
    },
    {
      "token": "i",
      "start_offset": 2,
      "end_offset": 3,
      "type": "word",
      "position": 4
    },
    {
      "token": "ic",
      "start_offset": 2,
      "end_offset": 4,
      "type": "word",
      "position": 5
    },
    {
      "token": "c",
      "start_offset": 3,
      "end_offset": 4,
      "type": "word",
      "position": 6
    },
    {
      "token": "ck",
      "start_offset": 3,
      "end_offset": 5,
      "type": "word",
      "position": 7
    },
    {
      "token": "k",
      "start_offset": 4,
      "end_offset": 5,
      "type": "word",
      "position": 8
    },
    {
      "token": "k ",
      "start_offset": 4,
      "end_offset": 6,
      "type": "word",
      "position": 9
    },
    {
      "token": " ",
      "start_offset": 5,
      "end_offset": 6,
      "type": "word",
      "position": 10
    },
    {
      "token": " F",
      "start_offset": 5,
      "end_offset": 7,
      "type": "word",
      "position": 11
    },
    {
      "token": "F",
      "start_offset": 6,
      "end_offset": 7,
      "type": "word",
      "position": 12
    },
    {
      "token": "Fo",
      "start_offset": 6,
      "end_offset": 8,
      "type": "word",
      "position": 13
    },
    {
      "token": "o",
      "start_offset": 7,
      "end_offset": 8,
      "type": "word",
      "position": 14
    },
    {
      "token": "ox",
      "start_offset": 7,
      "end_offset": 9,
      "type": "word",
      "position": 15
    },
    {
      "token": "x",
      "start_offset": 8,
      "end_offset": 9,
      "type": "word",
      "position": 16
    }
  ]
}
----------------------------
// TESTRESPONSE

/////////////////////


The above sentence would produce the following terms:

[source,text]
---------------------------
[ Q, Qu, u, ui, i, ic, c, ck, k, "k ", " ", " F", F, Fo, o, ox, x ]
---------------------------

[float]
=== Configuration

The `ngram` tokenizer accepts the following parameters:

[horizontal]
`min_gram`::
    Minimum length of characters in a gram.  Defaults to `1`.

`max_gram`::
    Maximum length of characters in a gram.  Defaults to `2`.

`token_chars`::

    Character classes that should be included in a token.  Elasticsearch
    will split on characters that don't belong to the classes specified.
    Defaults to `[]` (keep all characters).
+
Character classes may be any of the following:
+
* `letter` --      for example `a`, `b`, `ï` or `京`
* `digit` --       for example `3` or `7`
* `whitespace` --  for example `" "` or `"\n"`
* `punctuation` -- for example `!` or `"`
* `symbol` --      for example `$` or `√`

TIP:  It usually makes sense to set `min_gram` and `max_gram` to the same
value.  The smaller the length, the more documents will match but the lower
the quality of the matches.  The longer the length, the more specific the
matches.  A tri-gram (length `3`) is a good place to start.

The index level setting `index.max_ngram_diff` controls the maximum allowed
difference between `max_gram` and `min_gram`.

[float]
=== Example configuration

In this example, we configure the `ngram` tokenizer to treat letters and
digits as tokens, and to produce tri-grams (grams of length `3`):

[source,js]
----------------------------
PUT my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "tokenizer": "my_tokenizer"
        }
      },
      "tokenizer": {
        "my_tokenizer": {
          "type": "ngram",
          "min_gram": 3,
          "max_gram": 3,
          "token_chars": [
            "letter",
            "digit"
          ]
        }
      }
    }
  }
}

POST my_index/_analyze
{
  "analyzer": "my_analyzer",
  "text": "2 Quick Foxes."
}
----------------------------
// CONSOLE

/////////////////////

[source,js]
----------------------------
{
  "tokens": [
    {
      "token": "Qui",
      "start_offset": 2,
      "end_offset": 5,
      "type": "word",
      "position": 0
    },
    {
      "token": "uic",
      "start_offset": 3,
      "end_offset": 6,
      "type": "word",
      "position": 1
    },
    {
      "token": "ick",
      "start_offset": 4,
      "end_offset": 7,
      "type": "word",
      "position": 2
    },
    {
      "token": "Fox",
      "start_offset": 8,
      "end_offset": 11,
      "type": "word",
      "position": 3
    },
    {
      "token": "oxe",
      "start_offset": 9,
      "end_offset": 12,
      "type": "word",
      "position": 4
    },
    {
      "token": "xes",
      "start_offset": 10,
      "end_offset": 13,
      "type": "word",
      "position": 5
    }
  ]
}
----------------------------
// TESTRESPONSE

/////////////////////


The above example produces the following terms:

[source,text]
---------------------------
[ Qui, uic, ick, Fox, oxe, xes ]
---------------------------