OpenSearch/docs/reference/analysis/analyzers/keyword-analyzer.asciidoc

[[analysis-keyword-analyzer]]
=== Keyword Analyzer

The `keyword` analyzer is a ``noop'' analyzer which returns the entire input
string as a single token.

[float]
=== Example output

[source,js]
---------------------------
POST _analyze
{
  "analyzer": "keyword",
  "text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}
---------------------------
// CONSOLE

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

[source,js]
----------------------------
{
  "tokens": [
    {
      "token": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone.",
      "start_offset": 0,
      "end_offset": 56,
      "type": "word",
      "position": 0
    }
  ]
}
----------------------------
// TESTRESPONSE

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


The above sentence would produce the following single term:

[source,text]
---------------------------
[ The 2 QUICK Brown-Foxes jumped over the lazy dog's bone. ]
---------------------------

[float]
=== Configuration

The `keyword` analyzer is not configurable.

[float]
=== Definition

The `keyword` analyzer consists of:

Tokenizer::
* <<analysis-keyword-tokenizer,Keyword Tokenizer>>

If you need to customize the `keyword` analyzer then you need to
recreate it as a `custom` analyzer and modify it, usually by adding
token filters. Usually, you should prefer the
<<keyword, Keyword type>> when you want strings that are not split
into tokens, but just in case you need it, this would recreate the
built-in `keyword` analyzer and you can use it as a starting point
for further customization:

[source,js]
----------------------------------------------------
PUT /keyword_example
{
  "settings": {
    "analysis": {
      "analyzer": {
        "rebuilt_keyword": {
          "tokenizer": "keyword",
          "filter": [         <1>
          ]
        }
      }
    }
  }
}
----------------------------------------------------
// CONSOLE
// TEST[s/\n$/\nstartyaml\n  - compare_analyzers: {index: keyword_example, first: keyword, second: rebuilt_keyword}\nendyaml\n/]
<1> You'd add any token filters here.