## 9.13. Text Search Functions and Operators

Table 9.40, Table 9.41 and Table 9.42 summarize the functions and operators that are provided for full text searching. See Chapter 12 for a detailed explanation of Postgres Pro's text search facility.

Table 9.40. Text Search Operators

Operator | Return Type | Description | Example | Result
--- | --- | --- | --- | ---
`@@` | `boolean` | `tsvector` matches `tsquery` ? | `to_tsvector('fat cats ate rats') @@ to_tsquery('cat & rat')` | `t`
`@@@` | `boolean` | deprecated synonym for `@@` | `to_tsvector('fat cats ate rats') @@@ to_tsquery('cat & rat')` | `t`
`||` | `tsvector` | concatenate `tsvector`s | `'a:1 b:2'::tsvector || 'c:1 d:2 b:3'::tsvector` | `'a':1 'b':2,5 'c':3 'd':4`
`&&` | `tsquery` | AND `tsquery`s together | `'fat | rat'::tsquery && 'cat'::tsquery` | `( 'fat' | 'rat' ) & 'cat'`
`||` | `tsquery` | OR `tsquery`s together | `'fat | rat'::tsquery || 'cat'::tsquery` | `( 'fat' | 'rat' ) | 'cat'`
`!!` | `tsquery` | negate a `tsquery` | `!! 'cat'::tsquery` | `!'cat'`
`<->` | `tsquery` | `tsquery` followed by `tsquery` | `to_tsquery('fat') <-> to_tsquery('rat')` | `'fat' <-> 'rat'`
`@>` | `boolean` | `tsquery` contains another ? | `'cat'::tsquery @> 'cat & rat'::tsquery` | `f`
`<@` | `boolean` | `tsquery` is contained in ? | `'cat'::tsquery <@ 'cat & rat'::tsquery` | `t`

### Note

The `tsquery` containment operators consider only the lexemes listed in the two queries, ignoring the combining operators.

In addition to the operators shown in the table, the ordinary B-tree comparison operators (`=`, `<`, etc) are defined for types `tsvector` and `tsquery`. These are not very useful for text searching but allow, for example, unique indexes to be built on columns of these types.

Table 9.41. Text Search Functions

Function | Return Type | Description | Example | Result
--- | --- | --- | --- | ---
``array_to_tsvector(`text[]`)`` | `tsvector` | convert array of lexemes to `tsvector` | `array_to_tsvector('{fat,cat,rat}'::text[])` | `'cat' 'fat' 'rat'`
``get_current_ts_config()`` | `regconfig` | get default text search configuration | `get_current_ts_config()` | `english`
``length(`tsvector`)`` | `integer` | number of lexemes in `tsvector` | `length('fat:2,4 cat:3 rat:5A'::tsvector)` | `3`
``numnode(`tsquery`)`` | `integer` | number of lexemes plus operators in `tsquery` | `numnode('(fat & rat) | cat'::tsquery)` | `5`
``plainto_tsquery([ `config` `regconfig` , ] `query` `text`)`` | `tsquery` | produce `tsquery` ignoring punctuation | `plainto_tsquery('english', 'The Fat Rats')` | `'fat' & 'rat'`
``phraseto_tsquery([ `config` `regconfig` , ] `query` `text`)`` | `tsquery` | produce `tsquery` that searches for a phrase, ignoring punctuation | `phraseto_tsquery('english', 'The Fat Rats')` | `'fat' <-> 'rat'`
``websearch_to_tsquery([ `config` `regconfig` , ] `query` `text`)`` | `tsquery` | produce `tsquery` from a web search style query | `websearch_to_tsquery('english', '"fat rat" or rat')` | `'fat' <-> 'rat' | 'rat'`
``querytree(`query` `tsquery`)`` | `text` | get indexable part of a `tsquery` | `querytree('foo & ! bar'::tsquery)` | `'foo'`
``setweight(`vector` `tsvector`, `weight` `"char"`)`` | `tsvector` | assign `weight` to each element of `vector` | `setweight('fat:2,4 cat:3 rat:5B'::tsvector, 'A')` | `'cat':3A 'fat':2A,4A 'rat':5A`
``setweight(`vector` `tsvector`, `weight` `"char"`, `lexemes` `text[]`)`` | `tsvector` | assign `weight` to elements of `vector` that are listed in `lexemes` | `setweight('fat:2,4 cat:3 rat:5B'::tsvector, 'A', '{cat,rat}')` | `'cat':3A 'fat':2,4 'rat':5A`
``strip(`tsvector`)`` | `tsvector` | remove positions and weights from `tsvector` | `strip('fat:2,4 cat:3 rat:5A'::tsvector)` | `'cat' 'fat' 'rat'`
``to_tsquery([ `config` `regconfig` , ] `query` `text`)`` | `tsquery` | normalize words and convert to `tsquery` | `to_tsquery('english', 'The & Fat & Rats')` | `'fat' & 'rat'`
``to_tsvector([ `config` `regconfig` , ] `document` `text`)`` | `tsvector` | reduce document text to `tsvector` | `to_tsvector('english', 'The Fat Rats')` | `'fat':2 'rat':3`
``to_tsvector([ `config` `regconfig` , ] `document` `json(b)`)`` | `tsvector` | reduce each string value in the document to a `tsvector`, and then concatenate those in document order to produce a single `tsvector` | `to_tsvector('english', '{"a": "The Fat Rats"}'::json)` | `'fat':2 'rat':3`
``json(b)_to_tsvector([ `config` `regconfig`, ] `document` `json(b)`, `filter` `json(b)`)`` | `tsvector` | reduce each value in the document, specified by `filter` to a `tsvector`, and then concatenate those in document order to produce a single `tsvector`. `filter` is a `jsonb` array, that enumerates what kind of elements need to be included into the resulting `tsvector`. Possible values for `filter` are `"string"` (to include all string values), `"numeric"` (to include all numeric values in the string format), `"boolean"` (to include all Boolean values in the string format `"true"`/`"false"`), `"key"` (to include all keys) or `"all"` (to include all above). These values can be combined together to include, e.g. all string and numeric values. | `json_to_tsvector('english', '{"a": "The Fat Rats", "b": 123}'::json, '["string", "numeric"]')` | `'123':5 'fat':2 'rat':3`
``ts_delete(`vector` `tsvector`, `lexeme` `text`)`` | `tsvector` | remove given `lexeme` from `vector` | `ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, 'fat')` | `'cat':3 'rat':5A`
``ts_delete(`vector` `tsvector`, `lexemes` `text[]`)`` | `tsvector` | remove any occurrence of lexemes in `lexemes` from `vector` | `ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, ARRAY['fat','rat'])` | `'cat':3`
``ts_filter(`vector` `tsvector`, `weights` `"char"[]`)`` | `tsvector` | select only elements with given `weights` from `vector` | `ts_filter('fat:2,4 cat:3b rat:5A'::tsvector, '{a,b}')` | `'cat':3B 'rat':5A`
``ts_headline([ `config` `regconfig`, ] `document` `text`, `query` `tsquery` [, `options` `text` ])`` | `text` | display a query match | `ts_headline('x y z', 'z'::tsquery)` | `x y <b>z</b>`
``ts_headline([ `config` `regconfig`, ] `document` `json(b)`, `query` `tsquery` [, `options` `text` ])`` | `text` | display a query match | `ts_headline('{"a":"x y z"}'::json, 'z'::tsquery)` | `{"a":"x y <b>z</b>"}`
``ts_rank([ `weights` `float4[]`, ] `vector` `tsvector`, `query` `tsquery` [, `normalization` `integer` ])`` | `float4` | rank document for query | `ts_rank(textsearch, query)` | `0.818`
``ts_rank_cd([ `weights` `float4[]`, ] `vector` `tsvector`, `query` `tsquery` [, `normalization` `integer` ])`` | `float4` | rank document for query using cover density | `ts_rank_cd('{0.1, 0.2, 0.4, 1.0}', textsearch, query)` | `2.01317`
``ts_rewrite(`query` `tsquery`, `target` `tsquery`, `substitute` `tsquery`)`` | `tsquery` | replace `target` with `substitute` within query | `ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'foo|bar'::tsquery)` | `'b' & ( 'foo' | 'bar' )`
``ts_rewrite(`query` `tsquery`, `select` `text`)`` | `tsquery` | replace using targets and substitutes from a `SELECT` command | `SELECT ts_rewrite('a & b'::tsquery, 'SELECT t,s FROM aliases')` | `'b' & ( 'foo' | 'bar' )`
``tsquery_phrase(`query1` `tsquery`, `query2` `tsquery`)`` | `tsquery` | make query that searches for `query1` followed by `query2` (same as `<->` operator) | `tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'))` | `'fat' <-> 'cat'`
``tsquery_phrase(`query1` `tsquery`, `query2` `tsquery`, `distance` `integer`)`` | `tsquery` | make query that searches for `query1` followed by `query2` at distance `distance` | `tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'), 10)` | `'fat' <10> 'cat'`
``tsvector_to_array(`tsvector`)`` | `text[]` | convert `tsvector` to array of lexemes | `tsvector_to_array('fat:2,4 cat:3 rat:5A'::tsvector)` | `{cat,fat,rat}`
``tsvector_update_trigger()`` | `trigger` | trigger function for automatic `tsvector` column update | `CREATE TRIGGER ... tsvector_update_trigger(tsvcol, 'pg_catalog.swedish', title, body)` | ``
``tsvector_update_trigger_column()`` | `trigger` | trigger function for automatic `tsvector` column update | `CREATE TRIGGER ... tsvector_update_trigger_column(tsvcol, configcol, title, body)` | ``
``unnest(`tsvector`, OUT `lexeme` `text`, OUT `positions` `smallint[]`, OUT `weights` `text`)`` | `setof record` | expand a `tsvector` to a set of rows | `unnest('fat:2,4 cat:3 rat:5A'::tsvector)` | `(cat,{3},{D}) ...`

### Note

All the text search functions that accept an optional `regconfig` argument will use the configuration specified by default_text_search_config when that argument is omitted.

The functions in Table 9.42 are listed separately because they are not usually used in everyday text searching operations. They are helpful for development and debugging of new text search configurations.

Table 9.42. Text Search Debugging Functions

Function | Return Type | Description | Example | Result
--- | --- | --- | --- | ---
``ts_debug([ `config` `regconfig`, ] `document` `text`, OUT `alias` `text`, OUT `description` `text`, OUT `token` `text`, OUT `dictionaries` `regdictionary[]`, OUT `dictionary` `regdictionary`, OUT `lexemes` `text[]`)`` | `setof record` | test a configuration | `ts_debug('english', 'The Brightest supernovaes')` | `(asciiword,"Word, all ASCII",The,{english_stem},english_stem,{}) ...`
``ts_lexize(`dict` `regdictionary`, `token` `text`)`` | `text[]` | test a dictionary | `ts_lexize('english_stem', 'stars')` | `{star}`
``ts_parse(`parser_name` `text`, `document` `text`, OUT `tokid` `integer`, OUT `token` `text`)`` | `setof record` | test a parser | `ts_parse('default', 'foo - bar')` | `(1,foo) ...`
``ts_parse(`parser_oid` `oid`, `document` `text`, OUT `tokid` `integer`, OUT `token` `text`)`` | `setof record` | test a parser | `ts_parse(3722, 'foo - bar')` | `(1,foo) ...`
``ts_token_type(`parser_name` `text`, OUT `tokid` `integer`, OUT `alias` `text`, OUT `description` `text`)`` | `setof record` | get token types defined by parser | `ts_token_type('default')` | `(1,asciiword,"Word, all ASCII") ...`
``ts_token_type(`parser_oid` `oid`, OUT `tokid` `integer`, OUT `alias` `text`, OUT `description` `text`)`` | `setof record` | get token types defined by parser | `ts_token_type(3722)` | `(1,asciiword,"Word, all ASCII") ...`
``ts_stat(`sqlquery` `text`, [ `weights` `text`, ] OUT `word` `text`, OUT `ndoc` `integer`, OUT `nentry` `integer`)`` | `setof record` | get statistics of a `tsvector` column | `ts_stat('SELECT vector from apod')` | `(foo,10,15) ...`