--- title: Edge n-gram token filter description: Forms an n-gram of a specified length from the beginning of a token. For example, you can use the edge_ngram token filter to change quick to qu. When... url: https://www.elastic.co/docs/reference/text-analysis/analysis-edgengram-tokenfilter products: - Elasticsearch --- # Edge n-gram token filter Forms an [n-gram](https://en.wikipedia.org/wiki/N-gram) of a specified length from the beginning of a token. For example, you can use the `edge_ngram` token filter to change `quick` to `qu`. When not customized, the filter creates 1-character edge n-grams by default. This filter uses Lucene’s [EdgeNGramTokenFilter](https://lucene.apache.org/core/10_0_0/analysis/common/org/apache/lucene/analysis/ngram/EdgeNGramTokenFilter.md). The `edge_ngram` filter is similar to the [`ngram` token filter](https://www.elastic.co/docs/reference/text-analysis/analysis-ngram-tokenizer). However, the `edge_ngram` only outputs n-grams that start at the beginning of a token. These edge n-grams are useful for [search-as-you-type](https://www.elastic.co/docs/reference/elasticsearch/mapping-reference/search-as-you-type) queries. ## Example The following [analyze API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-indices-analyze) request uses the `edge_ngram` filter to convert `the quick brown fox jumps` to 1-character and 2-character edge n-grams: ```json { "tokenizer": "standard", "filter": [ { "type": "edge_ngram", "min_gram": 1, "max_gram": 2 } ], "text": "the quick brown fox jumps" } ``` The filter produces the following tokens: ```text [ t, th, q, qu, b, br, f, fo, j, ju ] ``` ## Add to an analyzer The following [create index API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-indices-create) request uses the `edge_ngram` filter to configure a new [custom analyzer](https://www.elastic.co/docs/manage-data/data-store/text-analysis/create-custom-analyzer). ```json { "settings": { "analysis": { "analyzer": { "standard_edge_ngram": { "tokenizer": "standard", "filter": [ "edge_ngram" ] } } } } } ``` ## Configurable parameters (Optional, integer) Maximum character length of a gram. For custom token filters, defaults to `2`. For the built-in `edge_ngram` filter, defaults to `1`. See [Limitations of the `max_gram` parameter](#analysis-edgengram-tokenfilter-max-gram-limits). (Optional, integer) Minimum character length of a gram. Defaults to `1`. (Optional, Boolean) Emits original token when set to `true`. Defaults to `false`. This setting was deprecated in 8.16.0. (Optional, string) Indicates whether to truncate tokens from the `front` or `back`. Defaults to `front`. ## Customize To customize the `edge_ngram` filter, duplicate it to create the basis for a new custom token filter. You can modify the filter using its configurable parameters. For example, the following request creates a custom `edge_ngram` filter that forms n-grams between 3-5 characters. ```json { "settings": { "analysis": { "analyzer": { "default": { "tokenizer": "whitespace", "filter": [ "3_5_edgegrams" ] } }, "filter": { "3_5_edgegrams": { "type": "edge_ngram", "min_gram": 3, "max_gram": 5 } } } } } ``` ## Limitations of the `max_gram` parameter The `edge_ngram` filter’s `max_gram` value limits the character length of tokens. When the `edge_ngram` filter is used with an index analyzer, this means search terms longer than the `max_gram` length may not match any indexed terms. For example, if the `max_gram` is `3`, searches for `apple` won’t match the indexed term `app`. To account for this, you can use the [`truncate`](https://www.elastic.co/docs/reference/text-analysis/analysis-truncate-tokenfilter) filter with a search analyzer to shorten search terms to the `max_gram` character length. However, this could return irrelevant results. For example, if the `max_gram` is `3` and search terms are truncated to three characters, the search term `apple` is shortened to `app`. This means searches for `apple` return any indexed terms matching `app`, such as `apply`, `snapped`, and `apple`. We recommend testing both approaches to see which best fits your use case and desired search experience.