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<h1 align="center">The tsearch2 Reference</h1>
<p align="center">
Brandon Craig Rhodes<br>30 June 2003 (edited by Oleg Bartunov, 2 Aug 2003).
</p><p>
This Reference documents the user types and functions
of the tsearch2 module for PostgreSQL.
An introduction to the module is provided
by the <a href="http://www.sai.msu.su/%7Emegera/postgres/gist/tsearch/V2/docs/tsearch2-guide.html">tsearch2 Guide</a>,
a companion document to this one.
You can retrieve a beta copy of the tsearch2 module from the
<a href="http://www.sai.msu.su/%7Emegera/postgres/gist/">GiST for PostgreSQL</a>
page -- look under the section entitled <i>Development History</i>
for the current version.
</p><h2><a name="vq">Vectors and Queries</a></h2>
<a name="vq">Vectors and queries both store lexemes,
but for different purposes.
A <tt>tsvector</tt> stores the lexemes
of the words that are parsed out of a document,
and can also remember the position of each word.
A <tt>tsquery</tt> specifies a boolean condition among lexemes.
</a><p>
<a name="vq">Any of the following functions with a <tt><i>configuration</i></tt> argument
can use either an integer <tt>id</tt> or textual <tt>ts_name</tt>
to select a configuration;
if the option is omitted, then the current configuration is used.
For more information on the current configuration,
read the next section on Configurations.
</a></p><h3><a name="vq">Vector Operations</a></h3>
<dl><dt>
<a name="vq"> <tt>to_tsvector( <em>[</em><i>configuration</i>,<em>]</em>
<i>document</i> TEXT) RETURNS tsvector</tt>
</a></dt><dd>
<a name="vq"> Parses a document into tokens,
reduces the tokens to lexemes,
and returns a <tt>tsvector</tt> which lists the lexemes
together with their positions in the document.
For the best description of this process,
see the section on </a><a href="http://www.sai.msu.su/%7Emegera/postgres/gist/tsearch/V2/docs/tsearch2-guide.html#ps">Parsing and Stemming</a>
in the accompanying tsearch2 Guide.
</dd><dt>
<tt>strip(<i>vector</i> tsvector) RETURNS tsvector</tt>
</dt><dd>
Return a vector which lists the same lexemes
as the given <tt><i>vector</i></tt>,
but which lacks any information
about where in the document each lexeme appeared.
While the returned vector is thus useless for relevance ranking,
it will usually be much smaller.
</dd><dt>
<tt>setweight(<i>vector</i> tsvector, <i>letter</i>) RETURNS tsvector</tt>
</dt><dd>
This function returns a copy of the input vector
in which every location has been labelled
with either the <tt><i>letter</i></tt>
<tt>'A'</tt>, <tt>'B'</tt>, or <tt>'C'</tt>,
or the default label <tt>'D'</tt>
(which is the default with which new vectors are created,
and as such is usually not displayed).
These labels are retained when vectors are concatenated,
allowing words from different parts of a document
to be weighted differently by ranking functions.
</dd><dt>
<tt><i>vector1</i> || <i>vector2</i></tt>
</dt><dt class="br">
<tt>concat(<i>vector1</i> tsvector, <i>vector2</i> tsvector)
RETURNS tsvector</tt>
</dt><dd>
Returns a vector which combines the lexemes and position information
in the two vectors given as arguments.
Position weight labels (described in the previous paragraph)
are retained intact during the concatenation.
This has at least two uses.
First,
if some sections of your document
need be parsed with different configurations than others,
you can parse them separately
and concatenate the resulting vectors into one.
Second,
you can weight words from some sections of you document
more heavily than those from others by:
parsing the sections into separate vectors;
assigning the vectors different position labels
with the <tt>setweight()</tt> function;
concatenating them into a single vector;
and then providing a <tt><i>weights</i></tt> argument
to the <tt>rank()</tt> function
that assigns different weights to positions with different labels.
</dd><dt>
<tt>tsvector_size(<i>vector</i> tsvector) RETURNS INT4</tt>
</dt><dd>
Returns the number of lexemes stored in the vector.
</dd><dt>
<tt><i>text</i>::tsvector RETURNS tsvector</tt>
</dt><dd>
Directly casting text to a <tt>tsvector</tt>
allows you to directly inject lexemes into a vector,
with whatever positions and position weights you choose to specify.
The <tt><i>text</i></tt> should be formatted
like the vector would be printed by the output of a <tt>SELECT</tt>.
See the <a href="http://www.sai.msu.su/%7Emegera/postgres/gist/tsearch/V2/docs/tsearch2-guide.html#casting">Casting</a>
section in the Guide for details.
</dd></dl>
<h3>Query Operations</h3>
<dl><dt>
<tt>to_tsquery( <em>[</em><i>configuration</i>,<em>]</em>
<i>querytext</i> text) RETURNS tsvector</tt>
</dt><dd>
Parses a query,
which should be single words separated by the boolean operators
"<tt>&amp;</tt>"&nbsp;and,
"<tt>|</tt>"&nbsp;or,
and&nbsp;"<tt>!</tt>"&nbsp;not,
which can be grouped using parenthesis.
Each word is reduced to a lexeme using the current
or specified configuration.
</dd><dt>
<tt>querytree(<i>query</i> tsquery) RETURNS text</tt>
</dt><dd>
This might return a textual representation of the given query.
</dd><dt>
<tt><i>text</i>::tsquery RETURNS tsquery</tt>
</dt><dd>
Directly casting text to a <tt>tsquery</tt>
allows you to directly inject lexemes into a query,
with whatever positions and position weight flags you choose to specify.
The <tt><i>text</i></tt> should be formatted
like the query would be printed by the output of a <tt>SELECT</tt>.
See the <a href="http://www.sai.msu.su/%7Emegera/postgres/gist/tsearch/V2/docs/tsearch2-guide.html#casting">Casting</a>
section in the Guide for details.
</dd></dl>
<h2><a name="configurations">Configurations</a></h2>
A configuration specifies all of the equipment necessary
to transform a document into a <tt>tsvector</tt>:
the parser that breaks its text into tokens,
and the dictionaries which then transform each token into a lexeme.
Every call to <tt>to_tsvector()</tt> (described above)
uses a configuration to perform its processing.
Three configurations come with tsearch2:
<ul>
<li><b>default</b> -- Indexes words and numbers,
using the <i>en_stem</i> English Snowball stemmer for Latin-alphabet words
and the <i>simple</i> dictionary for all others.
</li><li><b>default_russian</b> -- Indexes words and numbers,
using the <i>en_stem</i> English Snowball stemmer for Latin-alphabet words
and the <i>ru_stem</i> Russian Snowball dictionary for all others.
</li><li><b>simple</b> -- Processes both words and numbers
with the <i>simple</i> dictionary,
which neither discards any stop words nor alters them.
</li></ul>
The tsearch2 modules initially chooses your current configuration
by looking for your current locale in the <tt>locale</tt> field
of the <tt>pg_ts_cfg</tt> table described below.
You can manipulate the current configuration yourself with these functions:
<dl><dt>
<tt>set_curcfg( <i>id</i> INT <em>|</em> <i>ts_name</i> TEXT
) RETURNS VOID</tt>
</dt><dd>
Set the current configuration used by <tt>to_tsvector</tt>
and <tt>to_tsquery</tt>.
</dd><dt>
<tt>show_curcfg() RETURNS INT4</tt>
</dt><dd>
Returns the integer <tt>id</tt> of the current configuration.
</dd></dl>
<p>
Each configuration is defined by a record in the <tt>pg_ts_cfg</tt> table:
</p><pre>create table pg_ts_cfg (
id int not null primary key,
ts_name text not null,
prs_name text not null,
locale text
);</pre>
The <tt>id</tt> and <tt>ts_name</tt> are unique values
which identify the configuration;
the <tt>prs_name</tt> specifies which parser the configuration uses.
Once this parser has split document text into tokens,
the type of each resulting token --
or, more specifically, the type's <tt>tok_alias</tt>
as specified in the parser's <tt>lexem_type()</tt> table --
is searched for together with the configuration's <tt>ts_name</tt>
in the <tt>pg_ts_cfgmap</tt> table:
<pre>create table pg_ts_cfgmap (
ts_name text not null,
tok_alias text not null,
dict_name text[],
primary key (ts_name,tok_alias)
);</pre>
Those tokens whose types are not listed are discarded.
The remaining tokens are assigned integer positions,
starting with 1 for the first token in the document,
and turned into lexemes with the help of the dictionaries
whose names are given in the <tt>dict_name</tt> array for their type.
These dictionaries are tried in order,
stopping either with the first one to return a lexeme for the token,
or discarding the token if no dictionary returns a lexeme for it.
<h2><a name="testing">Testing</a></h2>
Function <tt>ts_debug</tt> allows easy testing of your <b>current</b> configuration.
You may always test another configuration using <tt>set_curcfg</tt> function.
<p>
Example:
</p><pre>apod=# select * from ts_debug('Tsearch module for PostgreSQL 7.3.3');
ts_name | tok_type | description | token | dict_name | tsvector
---------+----------+-------------+------------+-----------+--------------
default | lword | Latin word | Tsearch | {en_stem} | 'tsearch'
default | lword | Latin word | module | {en_stem} | 'modul'
default | lword | Latin word | for | {en_stem} |
default | lword | Latin word | PostgreSQL | {en_stem} | 'postgresql'
default | version | VERSION | 7.3.3 | {simple} | '7.3.3'
</pre>
Here:
<br>
<ul>
<li>tsname - configuration name
</li><li>tok_type - token type
</li><li>description - human readable name of tok_type
</li><li>token - parser's token
</li><li>dict_name - dictionary used for the token
</li><li>tsvector - final result</li></ul>
<h2><a name="parsers">Parsers</a></h2>
Each parser is defined by a record in the <tt>pg_ts_parser</tt> table:
<pre>create table pg_ts_parser (
prs_name text not null,
prs_start regprocedure not null,
prs_nexttoken regprocedure not null,
prs_end regprocedure not null,
prs_headline regprocedure not null,
prs_lextype regprocedure not null,
prs_comment text
);</pre>
The <tt>prs_name</tt> uniquely identify the parser,
while <tt>prs_comment</tt> usually describes its name and version
for the reference of users.
The other items identify the low-level functions
which make the parser operate,
and are only of interest to someone writing a parser of their own.
<p>
The tsearch2 module comes with one parser named <tt>default</tt>
which is suitable for parsing most plain text and HTML documents.
</p><p>
Each <tt><i>parser</i></tt> argument below
must designate a parser with <tt><i>prs_name</i></tt>;
the current parser is used when this argument is omitted.
</p><dl><dt>
<tt>CREATE FUNCTION set_curprs(<i>parser</i>) RETURNS VOID</tt>
</dt><dd>
Selects a current parser
which will be used when any of the following functions
are called without a parser as an argument.
</dd><dt>
<tt>CREATE FUNCTION token_type(
<em>[</em> <i>parser</i> <em>]</em>
) RETURNS SETOF tokentype</tt>
</dt><dd>
Returns a table which defines and describes
each kind of token the parser may produce as output.
For each token type the table gives the <tt>tokid</tt>
which the parser will label each token of that type,
the <tt>alias</tt> which names the token type,
and a short description <tt>descr</tt> for the user to read.
</dd><dt>
<tt>CREATE FUNCTION parse(
<em>[</em> <i>parser</i>, <em>]</em> <i>document</i> TEXT
) RETURNS SETOF tokenout</tt>
</dt><dd>
Parses the given document and returns a series of records,
one for each token produced by parsing.
Each token includes a <tt>tokid</tt> giving its type
and a <tt>lexem</tt> which gives its content.
</dd></dl>
<h2><a name="dictionaries">Dictionaries</a></h2>
Dictionaries take textual tokens as input,
usually those produced by a parser,
and return lexemes which are usually some reduced form of the token.
Among the dictionaries which come installed with tsearch2 are:
<ul>
<li><b>simple</b> simply folds uppercase letters to lowercase
before returning the word.
</li><li><b>en_stem</b> runs an English Snowball stemmer on each word
that attempts to reduce the various forms of a verb or noun
to a single recognizable form.
</li><li><b>ru_stem</b> runs a Russian Snowball stemmer on each word.
</li></ul>
Each dictionary is defined by an entry in the <tt>pg_ts_dict</tt> table:
<pre>CREATE TABLE pg_ts_dict (
dict_name text not null,
dict_init regprocedure,
dict_initoption text,
dict_lexize regprocedure not null,
dict_comment text
);</pre>
The <tt>dict_name</tt>
serve as unique identifiers for the dictionary.
The meaning of the <tt>dict_initoption</tt> varies among dictionaries,
but for the built-in Snowball dictionaries
it specifies a file from which stop words should be read.
The <tt>dict_comment</tt> is a human-readable description of the dictionary.
The other fields are internal function identifiers
useful only to developers trying to implement their own dictionaries.
<p>
The argument named <tt><i>dictionary</i></tt>
in each of the following functions
should be <tt>dict_name</tt>
identifying which dictionary should be used for the operation;
if omitted then the current dictionary is used.
</p><dl><dt>
<tt>CREATE FUNCTION set_curdict(<i>dictionary</i>) RETURNS VOID</tt>
</dt><dd>
Selects a current dictionary for use by functions
that do not select a dictionary explicitly.
</dd><dt>
<tt>CREATE FUNCTION lexize(
<em>[</em> <i>dictionary</i>, <em>]</em> <i>word</i> text)
RETURNS TEXT[]</tt>
</dt><dd>
Reduces a single word to a lexeme.
Note that lexemes are arrays of zero or more strings,
since in some languages there might be several base words
from which an inflected form could arise.
</dd></dl>
<h2><a name="ranking">Ranking</a></h2>
Ranking attempts to measure how relevant documents are to particular queries
by inspecting the number of times each search word appears in the document,
and whether different search terms occur near each other.
Note that this information is only available in unstripped vectors --
ranking functions will only return a useful result
for a <tt>tsvector</tt> which still has position information!
<p>
Both of these ranking functions
take an integer <i>normalization</i> option
that specifies whether a document's length should impact its rank.
This is often desirable,
since a hundred-word document with five instances of a search word
is probably more relevant than a thousand-word document with five instances.
The option can have the values:
</p><ul>
<li><tt>0</tt> (the default) ignores document length.
</li><li><tt>1</tt> divides the rank by the logarithm of the length.
</li><li><tt>2</tt> divides the rank by the length itself.
</li></ul>
The two ranking functions currently available are:
<dl><dt>
<tt>CREATE FUNCTION rank(<br>
<em>[</em> <i>weights</i> float4[], <em>]</em>
<i>vector</i> tsvector, <i>query</i> tsquery,
<em>[</em> <i>normalization</i> int4 <em>]</em><br>
) RETURNS float4</tt>
</dt><dd>
This is the ranking function from the old version of OpenFTS,
and offers the ability to weight word instances more heavily
depending on how you have classified them.
The <i>weights</i> specify how heavily to weight each category of word:
<pre>{<i>D-weight</i>, <i>C-weight</i>, <i>B-weight</i>, <i>A-weight</i>}</pre>
If no weights are provided, then these defaults are used:
<pre>{0.1, 0.2, 0.4, 1.0}</pre>
Often weights are used to mark words from special areas of the document,
like the title or an initial abstract,
and make them more or less important than words in the document body.
</dd><dt>
<tt>CREATE FUNCTION rank_cd(<br>
<em>[</em> <i>K</i> int4, <em>]</em>
<i>vector</i> tsvector, <i>query</i> tsquery,
<em>[</em> <i>normalization</i> int4 <em>]</em><br>
) RETURNS float4</tt>
</dt><dd>
This function computes the cover density ranking
for the given document <i>vector</i> and <i>query</i>,
as described in Clarke, Cormack, and Tudhope's
"<a href="http://citeseer.nj.nec.com/clarke00relevance.html">Relevance Ranking for One to Three Term Queries</a>"
in the 1999 <i>Information Processing and Management</i>.
The value <i>K</i> is one of the values from their formula,
and defaults to&nbsp;<i>K</i>=4.
The examples in their paper <i>K</i>=16;
we can roughly describe the term
as stating how far apart two search terms can fall
before the formula begins penalizing them for lack of proximity.
</dd></dl>
<h2><a name="headlines">Headlines</a></h2>
<dl><dt>
<tt>CREATE FUNCTION headline(<br>
<em>[</em> <i>id</i> int4, <em>|</em> <i>ts_name</i> text, <em>]</em>
<i>document</i> text, <i>query</i> tsquery,
<em>[</em> <i>options</i> text <em>]</em><br>
) RETURNS text</tt>
</dt><dd>
Every form of the the <tt>headline()</tt> function
accepts a <tt>document</tt> along with a <tt>query</tt>,
and returns one or more ellipse-separated excerpts from the document
in which terms from the query are highlighted.
The configuration with which to parse the document
can be specified by either its <i>id</i> or <i>ts_name</i>;
if none is specified that the current configuration is used instead.
<p>
An <i>options</i> string if provided should be a comma-separated list
of one or more '<i>option</i><tt>=</tt><i>value</i>' pairs.
The available options are:
</p><ul>
<li><tt>StartSel</tt>, <tt>StopSel</tt> --
the strings with which query words appearing in the document
should be delimited to distinguish them from other excerpted words.
</li><li><tt>MaxWords</tt>, <tt>MinWords</tt> --
limits on the shortest and longest headlines you will accept.
</li><li><tt>ShortWord</tt> --
this prevents your headline from beginning or ending
with a word which has this many characters or less.
The default value of <tt>3</tt> should eliminate most English
conjunctions and articles.
</li></ul>
Any unspecified options receive these defaults:
<pre>StartSel=&lt;b&gt;, StopSel=&lt;/b&gt;, MaxWords=35, MinWords=15, ShortWord=3
</pre>
</dd></dl>
</body></html>
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