322 lines
6.7 KiB
Plaintext
322 lines
6.7 KiB
Plaintext
[[analysis-edgengram-tokenizer]]
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=== Edge NGram Tokenizer
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The `edge_ngram` tokenizer first breaks text down into words whenever it
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encounters one of a list of specified characters, then it emits
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https://en.wikipedia.org/wiki/N-gram[N-grams] of each word where the start of
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the N-gram is anchored to the beginning of the word.
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Edge N-Grams are useful for _search-as-you-type_ queries.
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TIP: When you need _search-as-you-type_ for text which has a widely known
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order, such as movie or song titles, the
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<<completion-suggester,completion suggester>> is a much more efficient
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choice than edge N-grams. Edge N-grams have the advantage when trying to
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autocomplete words that can appear in any order.
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[float]
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=== Example output
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With the default settings, the `edge_ngram` tokenizer treats the initial text as a
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single token and produces N-grams with minimum length `1` and maximum length
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`2`:
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[source,js]
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---------------------------
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POST _analyze
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{
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"tokenizer": "edge_ngram",
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"text": "Quick Fox"
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}
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---------------------------
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// CONSOLE
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/////////////////////
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[source,js]
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----------------------------
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{
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"tokens": [
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{
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"token": "Q",
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"start_offset": 0,
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"end_offset": 1,
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"type": "word",
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"position": 0
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},
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{
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"token": "Qu",
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"start_offset": 0,
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"end_offset": 2,
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"type": "word",
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"position": 1
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}
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]
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}
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----------------------------
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// TESTRESPONSE
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/////////////////////
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The above sentence would produce the following terms:
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[source,text]
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---------------------------
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[ Q, Qu ]
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---------------------------
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NOTE: These default gram lengths are almost entirely useless. You need to
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configure the `edge_ngram` before using it.
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[float]
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=== Configuration
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The `edge_ngram` tokenizer accepts the following parameters:
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[horizontal]
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`min_gram`::
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Minimum length of characters in a gram. Defaults to `1`.
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`max_gram`::
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Maximum length of characters in a gram. Defaults to `2`.
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`token_chars`::
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Character classes that should be included in a token. Elasticsearch
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will split on characters that don't belong to the classes specified.
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Defaults to `[]` (keep all characters).
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+
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Character classes may be any of the following:
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+
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* `letter` -- for example `a`, `b`, `ï` or `京`
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* `digit` -- for example `3` or `7`
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* `whitespace` -- for example `" "` or `"\n"`
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* `punctuation` -- for example `!` or `"`
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* `symbol` -- for example `$` or `√`
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[float]
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=== Example configuration
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In this example, we configure the `edge_ngram` tokenizer to treat letters and
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digits as tokens, and to produce grams with minimum length `2` and maximum
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length `10`:
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[source,js]
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----------------------------
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PUT my_index
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{
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"settings": {
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"analysis": {
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"analyzer": {
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"my_analyzer": {
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"tokenizer": "my_tokenizer"
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}
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},
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"tokenizer": {
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"my_tokenizer": {
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"type": "edge_ngram",
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"min_gram": 2,
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"max_gram": 10,
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"token_chars": [
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"letter",
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"digit"
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]
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}
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}
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}
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}
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}
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POST my_index/_analyze
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{
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"analyzer": "my_analyzer",
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"text": "2 Quick Foxes."
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}
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----------------------------
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// CONSOLE
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/////////////////////
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[source,js]
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----------------------------
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{
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"tokens": [
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{
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"token": "Qu",
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"start_offset": 2,
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"end_offset": 4,
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"type": "word",
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"position": 0
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},
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{
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"token": "Qui",
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"start_offset": 2,
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"end_offset": 5,
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"type": "word",
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"position": 1
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},
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{
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"token": "Quic",
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"start_offset": 2,
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"end_offset": 6,
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"type": "word",
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"position": 2
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},
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{
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"token": "Quick",
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"start_offset": 2,
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"end_offset": 7,
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"type": "word",
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"position": 3
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},
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{
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"token": "Fo",
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"start_offset": 8,
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"end_offset": 10,
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"type": "word",
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"position": 4
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},
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{
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"token": "Fox",
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"start_offset": 8,
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"end_offset": 11,
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"type": "word",
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"position": 5
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},
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{
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"token": "Foxe",
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"start_offset": 8,
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"end_offset": 12,
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"type": "word",
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"position": 6
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},
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{
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"token": "Foxes",
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"start_offset": 8,
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"end_offset": 13,
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"type": "word",
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"position": 7
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}
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]
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}
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----------------------------
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// TESTRESPONSE
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/////////////////////
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The above example produces the following terms:
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[source,text]
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---------------------------
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[ Qu, Qui, Quic, Quick, Fo, Fox, Foxe, Foxes ]
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---------------------------
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Usually we recommend using the same `analyzer` at index time and at search
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time. In the case of the `edge_ngram` tokenizer, the advice is different. It
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only makes sense to use the `edge_ngram` tokenizer at index time, to ensure
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that partial words are available for matching in the index. At search time,
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just search for the terms the user has typed in, for instance: `Quick Fo`.
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Below is an example of how to set up a field for _search-as-you-type_:
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[source,js]
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-----------------------------------
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PUT my_index
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{
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"settings": {
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"analysis": {
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"analyzer": {
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"autocomplete": {
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"tokenizer": "autocomplete",
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"filter": [
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"lowercase"
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]
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},
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"autocomplete_search": {
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"tokenizer": "lowercase"
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}
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},
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"tokenizer": {
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"autocomplete": {
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"type": "edge_ngram",
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"min_gram": 2,
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"max_gram": 10,
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"token_chars": [
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"letter"
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]
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}
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}
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}
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},
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"mappings": {
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"properties": {
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"title": {
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"type": "text",
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"analyzer": "autocomplete",
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"search_analyzer": "autocomplete_search"
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}
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}
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}
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}
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PUT my_index/_doc/1
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{
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"title": "Quick Foxes" <1>
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}
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POST my_index/_refresh
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GET my_index/_search
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{
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"query": {
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"match": {
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"title": {
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"query": "Quick Fo", <2>
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"operator": "and"
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}
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}
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}
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}
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-----------------------------------
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// CONSOLE
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<1> The `autocomplete` analyzer indexes the terms `[qu, qui, quic, quick, fo, fox, foxe, foxes]`.
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<2> The `autocomplete_search` analyzer searches for the terms `[quick, fo]`, both of which appear in the index.
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/////////////////////
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[source,js]
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----------------------------
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{
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"took": $body.took,
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"timed_out": false,
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"_shards": {
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"total": 1,
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"successful": 1,
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"skipped" : 0,
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"failed": 0
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},
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"hits": {
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"total" : {
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"value": 1,
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"relation": "eq"
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},
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"max_score": 0.5753642,
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"hits": [
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{
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"_index": "my_index",
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"_type": "_doc",
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"_id": "1",
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"_score": 0.5753642,
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"_source": {
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"title": "Quick Foxes"
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}
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}
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]
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}
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}
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----------------------------
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// TESTRESPONSE[s/"took".*/"took": "$body.took",/]
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/////////////////////
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