---
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.