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| Original file line number | Diff line number | Diff line change |
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| --- | ||
| layout: default | ||
| title: N-gram | ||
| parent: Token filters | ||
| nav_order: 290 | ||
| --- | ||
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| # N-gram token filter | ||
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| The `ngram` token filter is a powerful tool used to break down text into smaller components, known as _n-grams_, which can improve partial matching and fuzzy search capabilities. It works by splitting a token into smaller substrings of defined lengths. These filters are commonly used in search applications to support autocomplete, partial matches, and typo-tolerant search. For more information, see [Autocomplete functionality]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/autocomplete/) and [Did-you-mean]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/did-you-mean/). | ||
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| ## Parameters | ||
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| The `ngram` token filter can be configured with the following parameters. | ||
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| Parameter | Required/Optional | Data type | Description | ||
| :--- | :--- | :--- | :--- | ||
| `min_gram` | Optional | Integer | The minimum length of the n-grams. Default is `1`. | ||
| `max_gram` | Optional | Integer | The maximum length of the n-grams. Default is `2`. | ||
| `preserve_original` | Optional | Boolean | Whether to keep the original token as one of the outputs. Default is `false`. | ||
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| ## Example | ||
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| The following example request creates a new index named `ngram_example_index` and configures an analyzer with an `ngram` filter: | ||
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| ```json | ||
| PUT /ngram_example_index | ||
| { | ||
| "settings": { | ||
| "analysis": { | ||
| "filter": { | ||
| "ngram_filter": { | ||
| "type": "ngram", | ||
| "min_gram": 2, | ||
| "max_gram": 3 | ||
| } | ||
| }, | ||
| "analyzer": { | ||
| "ngram_analyzer": { | ||
| "type": "custom", | ||
| "tokenizer": "standard", | ||
| "filter": [ | ||
| "lowercase", | ||
| "ngram_filter" | ||
| ] | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| ## Generated tokens | ||
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| Use the following request to examine the tokens generated using the analyzer: | ||
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| ```json | ||
| POST /ngram_example_index/_analyze | ||
| { | ||
| "analyzer": "ngram_analyzer", | ||
| "text": "Search" | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| The response contains the generated tokens: | ||
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| ```json | ||
| { | ||
| "tokens": [ | ||
| { | ||
| "token": "se", | ||
| "start_offset": 0, | ||
| "end_offset": 6, | ||
| "type": "<ALPHANUM>", | ||
| "position": 0 | ||
| }, | ||
| { | ||
| "token": "sea", | ||
| "start_offset": 0, | ||
| "end_offset": 6, | ||
| "type": "<ALPHANUM>", | ||
| "position": 0 | ||
| }, | ||
| { | ||
| "token": "ea", | ||
| "start_offset": 0, | ||
| "end_offset": 6, | ||
| "type": "<ALPHANUM>", | ||
| "position": 0 | ||
| }, | ||
| { | ||
| "token": "ear", | ||
| "start_offset": 0, | ||
| "end_offset": 6, | ||
| "type": "<ALPHANUM>", | ||
| "position": 0 | ||
| }, | ||
| { | ||
| "token": "ar", | ||
| "start_offset": 0, | ||
| "end_offset": 6, | ||
| "type": "<ALPHANUM>", | ||
| "position": 0 | ||
| }, | ||
| { | ||
| "token": "arc", | ||
| "start_offset": 0, | ||
| "end_offset": 6, | ||
| "type": "<ALPHANUM>", | ||
| "position": 0 | ||
| }, | ||
| { | ||
| "token": "rc", | ||
| "start_offset": 0, | ||
| "end_offset": 6, | ||
| "type": "<ALPHANUM>", | ||
| "position": 0 | ||
| }, | ||
| { | ||
| "token": "rch", | ||
| "start_offset": 0, | ||
| "end_offset": 6, | ||
| "type": "<ALPHANUM>", | ||
| "position": 0 | ||
| }, | ||
| { | ||
| "token": "ch", | ||
| "start_offset": 0, | ||
| "end_offset": 6, | ||
| "type": "<ALPHANUM>", | ||
| "position": 0 | ||
| } | ||
| ] | ||
| } | ||
| ``` |