Using the annotated-text field

The annotated-text tokenizes text content as per the more common text field (see "limitations" below) but also injects any marked-up annotation tokens directly into the search index:

 PUT my-index-000001 { "mappings": { "properties": { "my_field": { "type": "annotated_text" } } } } 

Such a mapping would allow marked-up text eg wikipedia articles to be indexed as both text and structured tokens. The annotations use a markdown-like syntax using URL encoding of one or more values separated by the & symbol.

We can use the "_analyze" api to test how an example annotation would be stored as tokens in the search index:

GET my-index-000001/_analyze { "field": "my_field", "text":"Investors in [Apple](Apple+Inc.) rejoiced." } 

Response:

{ "tokens": [ { "token": "investors", "start_offset": 0, "end_offset": 9, "type": "<ALPHANUM>", "position": 0 }, { "token": "in", "start_offset": 10, "end_offset": 12, "type": "<ALPHANUM>", "position": 1 }, { "token": "Apple Inc.", "start_offset": 13, "end_offset": 18, "type": "annotation", "position": 2 }, { "token": "apple", "start_offset": 13, "end_offset": 18, "type": "<ALPHANUM>", "position": 2 }, { "token": "rejoiced", "start_offset": 19, "end_offset": 27, "type": "<ALPHANUM>", "position": 3 } ] } 

1. Note the whole annotation token Apple Inc. is placed, unchanged as a single token in the token stream and at the same position (position 2) as the text token (apple) it annotates.

We can now perform searches for annotations using regular term queries that don’t tokenize the provided search values. Annotations are a more precise way of matching as can be seen in this example where a search for Beck will not match Jeff Beck :

 # Example documents PUT my-index-000001/_doc/1 { "my_field": "[Beck](Beck) announced a new tour"<1> } PUT my-index-000001/_doc/2 { "my_field": "[Jeff Beck](Jeff+Beck&Guitarist) plays a strat"<2> } # Example search GET my-index-000001/_search { "query": { "term": { "my_field": "Beck" } } } 

1. As well as tokenising the plain text into single words e.g. beck, here we inject the single token value Beck at the same position as beck in the token stream.
2. Note annotations can inject multiple tokens at the same position - here we inject both the very specific value Jeff Beck and the broader term Guitarist. This enables broader positional queries e.g. finding mentions of a Guitarist near to strat.
3. A benefit of searching with these carefully defined annotation tokens is that a query for Beck will not match document 2 that contains the tokens jeff, beck and Jeff Beck

If using a sub-keyword field then the values are sorted in the same way as a keyword field’s values are sorted. By default, that means sorted with duplicates removed. So:

 PUT idx { "settings": { "index": { "mapping": { "source": { "mode": "synthetic" } } } }, "mappings": { "properties": { "text": { "type": "annotated_text", "fields": { "raw": { "type": "keyword" } } } } } } PUT idx/_doc/1 { "text": [ "the quick brown fox", "the quick brown fox", "jumped over the lazy dog" ] } 

Will become:

{ "text": [ "jumped over the lazy dog", "the quick brown fox" ] } 

If the annotated_text field sets store to true then order and duplicates are preserved.

 PUT idx { "settings": { "index": { "mapping": { "source": { "mode": "synthetic" } } } }, "mappings": { "properties": { "text": { "type": "annotated_text", "store": true } } } } PUT idx/_doc/1 { "text": [ "the quick brown fox", "the quick brown fox", "jumped over the lazy dog" ] } 

Will become:

{ "text": [ "the quick brown fox", "the quick brown fox", "jumped over the lazy dog" ] }