## 8.11. Text Search Types

PostgreSQL provides two data types that are designed to support full text search, which is the activity of searching through a collection of natural-language documents to locate those that best match a query. The `tsvector` type represents a document in a form optimized for text search; the `tsquery` type similarly represents a text query. Chapter 12 provides a detailed explanation of this facility, and Section 9.13 summarizes the related functions and operators.

### 8.11.1. `tsvector`

A `tsvector` value is a sorted list of distinct lexemes, which are words that have been normalized to merge different variants of the same word (see Chapter 12 for details). Sorting and duplicate-elimination are done automatically during input, as shown in this example:

    SELECT 'a fat cat sat on a mat and ate a fat rat'::tsvector;
                          tsvector
    ----------------------------------------------------
     'a' 'and' 'ate' 'cat' 'fat' 'mat' 'on' 'rat' 'sat'

To represent lexemes containing whitespace or punctuation, surround them with quotes:

    SELECT $$the lexeme '    ' contains spaces$$::tsvector;
                     tsvector
    -------------------------------------------
     '    ' 'contains' 'lexeme' 'spaces' 'the'

(We use dollar-quoted string literals in this example and the next one to avoid the confusion of having to double quote marks within the literals.) Embedded quotes and backslashes must be doubled:

    SELECT $$the lexeme 'Joe''s' contains a quote$$::tsvector;
                        tsvector
    ------------------------------------------------
     'Joe''s' 'a' 'contains' 'lexeme' 'quote' 'the'

Optionally, integer positions can be attached to lexemes:

    SELECT 'a:1 fat:2 cat:3 sat:4 on:5 a:6 mat:7 and:8 ate:9 a:10 fat:11 rat:12'::tsvector;
                                      tsvector
    -------------------------------------------------------------------​------------
     'a':1,6,10 'and':8 'ate':9 'cat':3 'fat':2,11 'mat':7 'on':5 'rat':12 'sat':4

A position normally indicates the source word's location in the document. Positional information can be used for proximity ranking. Position values can range from 1 to 16383; larger numbers are silently set to 16383. Duplicate positions for the same lexeme are discarded.

Lexemes that have positions can further be labeled with a weight, which can be `A`, `B`, `C`, or `D`. `D` is the default and hence is not shown on output:

    SELECT 'a:1A fat:2B,4C cat:5D'::tsvector;
              tsvector
    ----------------------------
     'a':1A 'cat':5 'fat':2B,4C

Weights are typically used to reflect document structure, for example by marking title words differently from body words. Text search ranking functions can assign different priorities to the different weight markers.

It is important to understand that the `tsvector` type itself does not perform any word normalization; it assumes the words it is given are normalized appropriately for the application. For example,

    SELECT 'The Fat Rats'::tsvector;
          tsvector
    --------------------
     'Fat' 'Rats' 'The'

For most English-text-searching applications the above words would be considered non-normalized, but `tsvector` doesn't care. Raw document text should usually be passed through `to_tsvector` to normalize the words appropriately for searching:

    SELECT to_tsvector('english', 'The Fat Rats');
       to_tsvector
    -----------------
     'fat':2 'rat':3

Again, see Chapter 12 for more detail.

### 8.11.2. `tsquery`

A `tsquery` value stores lexemes that are to be searched for, and can combine them using the Boolean operators `&` (AND), `|` (OR), and `!` (NOT), as well as the phrase search operator `<->` (FOLLOWED BY). There is also a variant `<`N`>` of the FOLLOWED BY operator, where `N` is an integer constant that specifies the distance between the two lexemes being searched for. `<->` is equivalent to `<1>`.

Parentheses can be used to enforce grouping of these operators. In the absence of parentheses, `!` (NOT) binds most tightly, `<->` (FOLLOWED BY) next most tightly, then `&` (AND), with `|` (OR) binding the least tightly.

Here are some examples:

    SELECT 'fat & rat'::tsquery;
        tsquery
    ---------------
     'fat' & 'rat'

    SELECT 'fat & (rat | cat)'::tsquery;
              tsquery
    ---------------------------
     'fat' & ( 'rat' | 'cat' )

    SELECT 'fat & rat & ! cat'::tsquery;
            tsquery
    ------------------------
     'fat' & 'rat' & !'cat'

Optionally, lexemes in a `tsquery` can be labeled with one or more weight letters, which restricts them to match only `tsvector` lexemes with one of those weights:

    SELECT 'fat:ab & cat'::tsquery;
        tsquery
    ------------------
     'fat':AB & 'cat'

Also, lexemes in a `tsquery` can be labeled with `*` to specify prefix matching:

    SELECT 'super:*'::tsquery;
      tsquery
    -----------
     'super':*

This query will match any word in a `tsvector` that begins with “super”.

Quoting rules for lexemes are the same as described previously for lexemes in `tsvector`; and, as with `tsvector`, any required normalization of words must be done before converting to the `tsquery` type. The `to_tsquery` function is convenient for performing such normalization:

    SELECT to_tsquery('Fat:ab & Cats');
        to_tsquery
    ------------------
     'fat':AB & 'cat'

Note that `to_tsquery` will process prefixes in the same way as other words, which means this comparison returns true:

    SELECT to_tsvector( 'postgraduate' ) @@ to_tsquery( 'postgres:*' );
     ?column?
    ----------
     t

because `postgres` gets stemmed to `postgr`:

    SELECT to_tsvector( 'postgraduate' ), to_tsquery( 'postgres:*' );
      to_tsvector  | to_tsquery
    ---------------+------------
     'postgradu':1 | 'postgr':*

which will match the stemmed form of `postgraduate`.