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843 lines
31 KiB
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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<html><head>
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<title>tsearch2 reference</title></head>
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<body>
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<h1 align="center">The tsearch2 Reference</h1>
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<p align="center">
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Brandon Craig Rhodes<br>30 June 2003 (edited by Oleg Bartunov, 2 Aug 2003).
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<br>Massive update for 8.2 release by Oleg Bartunov, October 2006
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</p>
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<p>
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This Reference documents the user types and functions
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of the tsearch2 module for PostgreSQL.
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An introduction to the module is provided
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by the <a href="http://www.sai.msu.su/~megera/postgres/gist/tsearch/V2/docs/tsearch2-guide.html">tsearch2 Guide</a>,
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a companion document to this one.
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</p>
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<h2>Table of Contents</h2>
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<blockquote>
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<a href="#vq">Vectors and Queries</a><br>
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<a href="#vqo">Vector Operations</a><br>
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<a href="#qo">Query Operations</a><br>
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<a href="#fts">Full Text Search Operator</a><br>
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<a href="#configurations">Configurations</a><br>
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<a href="#testing">Testing</a><br>
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<a href="#parsers">Parsers</a><br>
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<a href="#dictionaries">Dictionaries</a><br>
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<a href="#ranking">Ranking</a><br>
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<a href="#headlines">Headlines</a><br>
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<a href="#indexes">Indexes</a><br>
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<a href="#tz">Thesaurus dictionary</a><br>
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</blockquote>
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<h2><a name="vq">Vectors and Queries</a></h2>
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Vectors and queries both store lexemes,
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but for different purposes.
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A <tt>tsvector</tt> stores the lexemes
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of the words that are parsed out of a document,
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and can also remember the position of each word.
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A <tt>tsquery</tt> specifies a boolean condition among lexemes.
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<p>
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Any of the following functions with a <tt><i>configuration</i></tt> argument
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can use either an integer <tt>id</tt> or textual <tt>ts_name</tt>
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to select a configuration;
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if the option is omitted, then the current configuration is used.
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For more information on the current configuration,
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read the next section on Configurations.
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</p>
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<h3><a name="vqo">Vector Operations</a></h3>
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<dl><dt>
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<tt>to_tsvector( <em>[</em><i>configuration</i>,<em>]</em>
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<i>document</i> TEXT) RETURNS TSVECTOR</tt>
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</dt><dd>
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Parses a document into tokens,
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reduces the tokens to lexemes,
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and returns a <tt>tsvector</tt> which lists the lexemes
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together with their positions in the document.
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For the best description of this process,
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see the section on <a href="http://www.sai.msu.su/%7Emegera/postgres/gist/tsearch/V2/docs/tsearch2-guide.html#ps">Parsing and Stemming</a>
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in the accompanying tsearch2 Guide.
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</dd><dt>
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<tt>strip(<i>vector</i> TSVECTOR) RETURNS TSVECTOR</tt>
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</dt><dd>
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Return a vector which lists the same lexemes
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as the given <tt><i>vector</i></tt>,
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but which lacks any information
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about where in the document each lexeme appeared.
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While the returned vector is thus useless for relevance ranking,
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it will usually be much smaller.
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</dd><dt>
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<tt>setweight(<i>vector</i> TSVECTOR, <i>letter</i>) RETURNS TSVECTOR</tt>
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</dt><dd>
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This function returns a copy of the input vector
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in which every location has been labeled
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with either the <tt><i>letter</i></tt>
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<tt>'A'</tt>, <tt>'B'</tt>, or <tt>'C'</tt>,
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or the default label <tt>'D'</tt>
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(which is the default with which new vectors are created,
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and as such is usually not displayed).
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These labels are retained when vectors are concatenated,
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allowing words from different parts of a document
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to be weighted differently by ranking functions.
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</dd>
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<dt>
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<tt><i>vector1</i> || <i>vector2</i></tt><BR>
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<tt>concat(<i>vector1</i> TSVECTOR, <i>vector2</i> TSVECTOR)
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RETURNS TSVECTOR</tt>
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</dt><dd>
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Returns a vector which combines the lexemes and position information
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in the two vectors given as arguments.
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Position weight labels (described in the previous paragraph)
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are retained intact during the concatenation.
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This has at least two uses.
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First,
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if some sections of your document
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need be parsed with different configurations than others,
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you can parse them separately
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and concatenate the resulting vectors into one.
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Second,
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you can weight words from some sections of you document
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more heavily than those from others by:
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parsing the sections into separate vectors;
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assigning the vectors different position labels
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with the <tt>setweight()</tt> function;
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concatenating them into a single vector;
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and then providing a <tt><i>weights</i></tt> argument
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to the <tt>rank()</tt> function
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that assigns different weights to positions with different labels.
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</dd><dt>
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<tt>length(<i>vector</i> TSVECTOR) RETURNS INT4</tt>
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</dt><dd>
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Returns the number of lexemes stored in the vector.
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</dd><dt>
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<tt><i>text</i>::TSVECTOR RETURNS TSVECTOR</tt>
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</dt><dd>
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Directly casting text to a <tt>tsvector</tt>
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allows you to directly inject lexemes into a vector,
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with whatever positions and position weights you choose to specify.
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The <tt><i>text</i></tt> should be formatted
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like the vector would be printed by the output of a <tt>SELECT</tt>.
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See the <a href="http://www.sai.msu.su/~megera/postgres/gist/tsearch/V2/docs/tsearch2-guide.html#casting">Casting</a>
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section in the Guide for details.
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</dd><dt>
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<tt>tsearch2(<i>vector_column_name</i>[, (<i>my_filter_name</i> | <i>text_column_name1</i>) [...] ], <i>text_column_nameN</i>)</tt>
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</dt><dd>
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<tt>tsearch2()</tt> trigger used to automatically update <i>vector_column_name</i>, <i>my_filter_name</i>
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is the function name to preprocess <i>text_column_name</i>. There are can be many
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functions and text columns specified in <tt>tsearch2()</tt> trigger.
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The following rule used:
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function applied to all subsequent text columns until next function occurs.
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Example, function <tt>dropatsymbol</tt> replaces all entries of <tt>@</tt>
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sign by space.
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<pre>
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CREATE FUNCTION dropatsymbol(text) RETURNS text
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AS 'select replace($1, ''@'', '' '');'
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LANGUAGE SQL;
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CREATE TRIGGER tsvectorupdate BEFORE UPDATE OR INSERT
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ON tblMessages FOR EACH ROW EXECUTE PROCEDURE
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tsearch2(tsvector_column,dropatsymbol, strMessage);
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</pre>
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</dd>
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<dt>
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<tt>stat(<i>sqlquery</i> text [, <i>weight</i> text]) RETURNS SETOF statinfo</tt>
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</dt><dd>
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Here <tt>statinfo</tt> is a type, defined as
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<tt>
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CREATE TYPE statinfo as (<i>word</i> text, <i>ndoc</i> int4, <i>nentry</i> int4)
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</tt> and <i>sqlquery</i> is a query, which returns column <tt>tsvector</tt>.
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<P>
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This returns statistics (the number of documents <i>ndoc</i> and total number <i>nentry</i> of <i>word</i>
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in the collection) about column <i>vector</i> <tt>tsvector</tt>.
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Useful to check how good is your configuration and
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to find stop-words candidates.For example, find top 10 most frequent words:
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<pre>
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=# select * from stat('select vector from apod') order by ndoc desc, nentry desc,word limit 10;
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</pre>
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Optionally, one can specify <i>weight</i> to obtain statistics about words with specific weight.
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<pre>
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=# select * from stat('select vector from apod','a') order by ndoc desc, nentry desc,word limit 10;
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</pre>
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</dd>
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<dt>
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<tt>TSVECTOR < TSVECTOR</tt><BR>
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<tt>TSVECTOR <= TSVECTOR</tt><BR>
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<tt>TSVECTOR = TSVECTOR</tt><BR>
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<tt>TSVECTOR >= TSVECTOR</tt><BR>
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<tt>TSVECTOR > TSVECTOR</tt>
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</dt><dd>
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All btree operations defined for <tt>tsvector</tt> type. <tt>tsvectors</tt> compares
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with each other using lexicographical order.
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</dd>
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</dl>
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<h3><a name="qo">Query Operations</a></h3>
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<dl>
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<dt>
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<tt>to_tsquery( <em>[</em><i>configuration</i>,<em>]</em>
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<i>querytext</i> text) RETURNS TSQUERY[A</tt>
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</dt>
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<dd>
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Parses a query,
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which should be single words separated by the boolean operators
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"<tt>&</tt>" and,
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"<tt>|</tt>" or,
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and "<tt>!</tt>" not,
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which can be grouped using parenthesis.
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Each word is reduced to a lexeme using the current
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or specified configuration.
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Weight class can be assigned to each lexeme entry
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to restrict search region
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(see <tt>setweight</tt> for explanation), for example
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"<tt>fat:a & rats</tt>".
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</dd><dt>
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<dt>
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<tt>plainto_tsquery( <em>[</em><i>configuration</i>,<em>]</em>
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<i>querytext</i> text) RETURNS TSQUERY</tt>
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</dt>
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<dd>
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Transforms unformatted text to tsquery. It is the same as to_tsquery,
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but assumes "<tt>&</tt>" boolean operator between words and doesn't
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recognizes weight classes.
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</dd><dt>
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<tt>querytree(<i>query</i> TSQUERY) RETURNS text</tt>
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</dt><dd>
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This returns a query which actually used in searching in GiST index.
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</dd><dt>
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<tt><i>text</i>::TSQUERY RETURNS TSQUERY</tt>
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</dt><dd>
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Directly casting text to a <tt>tsquery</tt>
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allows you to directly inject lexemes into a query,
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with whatever positions and position weight flags you choose to specify.
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The <tt><i>text</i></tt> should be formatted
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like the query would be printed by the output of a <tt>SELECT</tt>.
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See the <a href="http://www.sai.msu.su/%7Emegera/postgres/gist/tsearch/V2/docs/tsearch2-guide.html#casting">Casting</a>
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section in the Guide for details.
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</dd>
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<dt>
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<tt>numnode(<i>query</i> TSQUERY) RETURNS INTEGER</tt>
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</dt><dd>
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This returns the number of nodes in query tree
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</dd><dt>
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<tt>TSQUERY && TSQUERY RETURNS TSQUERY</tt>
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</dt><dd>
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AND-ed TSQUERY
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</dd><dt>
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<tt>TSQUERY || TSQUERY RETURNS TSQUERY</tt>
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</dt> <dd>
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OR-ed TSQUERY
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</dd><dt>
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<tt>!! TSQUERY RETURNS TSQUERY</tt>
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</dt> <dd>
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negation of TSQUERY
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</dd>
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<dt>
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<tt>TSQUERY < TSQUERY</tt><BR>
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<tt>TSQUERY <= TSQUERY</tt><BR>
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<tt>TSQUERY = TSQUERY</tt><BR>
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<tt>TSQUERY >= TSQUERY</tt><BR>
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<tt>TSQUERY > TSQUERY</tt>
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</dt><dd>
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All btree operations defined for <tt>tsquery</tt> type. <tt>tsqueries</tt> compares
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with each other using lexicographical order.
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</dd>
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</dl>
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<h3>Query rewriting</h3>
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Query rewriting is a set of functions and operators for tsquery type.
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It allows to control search at query time without reindexing (opposite to thesaurus), for example,
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expand search using synonyms (new york, big apple, nyc, gotham).
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<P>
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<tt><b>rewrite()</b></tt> function changes original <i>query</i> by replacing <i>target</i> by <i>sample</i>.
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There are three possibilities to use <tt>rewrite()</tt> function. Notice, that arguments of <tt>rewrite()</tt>
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function can be column names of type <tt>tsquery</tt>.
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<pre>
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create table rw (q TSQUERY, t TSQUERY, s TSQUERY);
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insert into rw values('a & b','a', 'c');
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</pre>
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<dl>
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<dt> <tt>rewrite (<i>query</i> TSQUERY, <i>target</i> TSQUERY, <i>sample</i> TSQUERY) RETURNS TSQUERY</tt>
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</dt>
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<dd>
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<pre>
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=# select rewrite('a & b'::TSQUERY, 'a'::TSQUERY, 'c'::TSQUERY);
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rewrite
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-----------
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'c' & 'b'
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</pre>
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</dd>
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<dt> <tt>rewrite (ARRAY[<i>query</i> TSQUERY, <i>target</i> TSQUERY, <i>sample</i> TSQUERY]) RETURNS TSQUERY</tt>
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</dt>
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<dd>
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<pre>
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=# select rewrite(ARRAY['a & b'::TSQUERY, t,s]) from rw;
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rewrite
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-----------
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'c' & 'b'
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</pre>
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</dd>
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<dt> <tt>rewrite (<i>query</i> TSQUERY,'select <i>target</i> ,<i>sample</i> from test'::text) RETURNS TSQUERY</tt>
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</dt>
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<dd>
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<pre>
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=# select rewrite('a & b'::TSQUERY, 'select t,s from rw'::text);
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rewrite
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-----------
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'c' & 'b'
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</pre>
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</dd>
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</dl>
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Two operators defined for <tt>tsquery</tt> type:
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<dl>
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<dt><tt>TSQUERY @ TSQUERY</tt></dt>
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<dd>
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Returns <tt>TRUE</tt> if right agrument might contained in left argument.
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</dd>
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<dt><tt>TSQUERY ~ TSQUERY</tt></dt>
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<dd>
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Returns <tt>TRUE</tt> if left agrument might contained in right argument.
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</dd>
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</dl>
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To speed up these operators one can use GiST index with <tt>gist_tp_tsquery_ops</tt> opclass.
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<pre>
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create index qq on test_tsquery using gist (keyword gist_tp_tsquery_ops);
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</pre>
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<h2><a name="fts">Full Text Search operator</a></h2>
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<dl><dt>
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<tt>TSQUERY @@ TSVECTOR</tt><br>
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<tt>TSVECTOR @@ TSQUERY</tt>
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</dt>
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<dd>
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Returns <tt>TRUE</tt> if <tt>TSQUERY</tt> contained in <tt>TSVECTOR</tt> and
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<tt>FALSE</tt> otherwise.
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<pre>
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=# select 'cat & rat':: tsquery @@ 'a fat cat sat on a mat and ate a fat rat'::tsvector;
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?column?
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----------
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t
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=# select 'fat & cow':: tsquery @@ 'a fat cat sat on a mat and ate a fat rat'::tsvector;
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?column?
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----------
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f
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</pre>
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</dd>
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</dl>
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<h2><a name="configurations">Configurations</a></h2>
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A configuration specifies all of the equipment necessary
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to transform a document into a <tt>tsvector</tt>:
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the parser that breaks its text into tokens,
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and the dictionaries which then transform each token into a lexeme.
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Every call to <tt>to_tsvector(), to_tsquery()</tt> (described above)
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uses a configuration to perform its processing.
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Three configurations come with tsearch2:
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<ul>
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<li><b>default</b> -- Indexes words and numbers,
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using the <i>en_stem</i> English Snowball stemmer for Latin-alphabet words
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and the <i>simple</i> dictionary for all others.
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</li><li><b>default_russian</b> -- Indexes words and numbers,
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using the <i>en_stem</i> English Snowball stemmer for Latin-alphabet words
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and the <i>ru_stem</i> Russian Snowball dictionary for all others. It's default
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for <tt>ru_RU.KOI8-R</tt> locale.
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</li><li><b>utf8_russian</b> -- the same as <b>default_russian</b> but
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for <tt>ru_RU.UTF-8</tt> locale.
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</li><li><b>simple</b> -- Processes both words and numbers
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with the <i>simple</i> dictionary,
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which neither discards any stop words nor alters them.
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</li></ul>
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The tsearch2 modules initially chooses your current configuration
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by looking for your current locale in the <tt>locale</tt> field
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of the <tt>pg_ts_cfg</tt> table described below.
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You can manipulate the current configuration yourself with these functions:
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<dl><dt>
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<tt>set_curcfg( <i>id</i> INT <em>|</em> <i>ts_name</i> TEXT
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) RETURNS VOID</tt>
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</dt><dd>
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Set the current configuration used by <tt>to_tsvector</tt>
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and <tt>to_tsquery</tt>.
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</dd><dt>
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<tt>show_curcfg() RETURNS INT4</tt>
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</dt><dd>
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Returns the integer <tt>id</tt> of the current configuration.
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</dd></dl>
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<p>
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Each configuration is defined by a record in the <tt>pg_ts_cfg</tt> table:
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</p><pre>create table pg_ts_cfg (
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id int not null primary key,
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ts_name text not null,
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prs_name text not null,
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locale text
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);</pre>
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The <tt>id</tt> and <tt>ts_name</tt> are unique values
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which identify the configuration;
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the <tt>prs_name</tt> specifies which parser the configuration uses.
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Once this parser has split document text into tokens,
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the type of each resulting token --
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or, more specifically, the type's <tt>tok_alias</tt>
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as specified in the parser's <tt>lexem_type()</tt> table --
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is searched for together with the configuration's <tt>ts_name</tt>
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in the <tt>pg_ts_cfgmap</tt> table:
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<pre>create table pg_ts_cfgmap (
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ts_name text not null,
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tok_alias text not null,
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dict_name text[],
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primary key (ts_name,tok_alias)
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);</pre>
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Those tokens whose types are not listed are discarded.
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The remaining tokens are assigned integer positions,
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starting with 1 for the first token in the document,
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and turned into lexemes with the help of the dictionaries
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whose names are given in the <tt>dict_name</tt> array for their type.
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These dictionaries are tried in order,
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stopping either with the first one to return a lexeme for the token,
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or discarding the token if no dictionary returns a lexeme for it.
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<h2><a name="testing">Testing</a></h2>
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Function <tt>ts_debug</tt> allows easy testing of your <b>current</b> configuration.
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You may always test another configuration using <tt>set_curcfg</tt> function.
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<p>
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Example:
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</p><pre>apod=# select * from ts_debug('Tsearch module for PostgreSQL 7.3.3');
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ts_name | tok_type | description | token | dict_name | tsvector
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---------+----------+-------------+------------+-----------+--------------
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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>
|
|
|
|
Dictionary is a program, which accepts lexeme(s), usually those produced by a parser,
|
|
on input and returns:
|
|
<ul>
|
|
<li>array of lexeme(s) if input lexeme is known to the dictionary
|
|
<li>void array - dictionary knows lexeme, but it's stop word.
|
|
<li> NULL - dictionary doesn't recognized input lexeme
|
|
</ul>
|
|
Usually, dictionaries used for normalization of words ( ispell, stemmer dictionaries),
|
|
but see, for example, <tt>intdict</tt> dictionary (available from
|
|
<a href="http://www.sai.msu.su/~megera/postgres/gist/tsearch/V2/">Tsearch2</a> home page,
|
|
which controls indexing of integers.
|
|
|
|
<P>
|
|
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>ispell_template</b> - template for ispell dictionaries.
|
|
</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_koi8</b>, <b>ru_stem_utf8</b> runs a Russian Snowball stemmer on each word.
|
|
</li>
|
|
<li><b>synonym</b> - simple lexeme-to-lexeme replacement
|
|
</li>
|
|
<li><b>thesaurus_template</b> - template for <a href="#tz">thesaurus dictionary</a>. It's
|
|
phrase-to-phrase replacement
|
|
</li>
|
|
</ul>
|
|
|
|
<P>
|
|
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.
|
|
|
|
<blockquote>
|
|
<b>WARNING:</b> Data files, used by dictionaries, should be in <tt>server_encoding</tt> to
|
|
avoid possible problems !
|
|
</blockquote>
|
|
|
|
<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>
|
|
|
|
<h3>Using dictionaries template</h3>
|
|
Templates used to define new dictionaries, for example,
|
|
<pre>
|
|
INSERT INTO pg_ts_dict
|
|
(SELECT 'en_ispell', dict_init,
|
|
'DictFile="/usr/local/share/dicts/ispell/english.dict",'
|
|
'AffFile="/usr/local/share/dicts/ispell/english.aff",'
|
|
'StopFile="/usr/local/share/dicts/english.stop"',
|
|
dict_lexize
|
|
FROM pg_ts_dict
|
|
WHERE dict_name = 'ispell_template');
|
|
</pre>
|
|
|
|
<h3>Working with stop words</h3>
|
|
Ispell and snowball stemmers treat stop words differently:
|
|
<ul>
|
|
<li>ispell - normalize word and then lookups normalized form in stop-word file
|
|
<li>snowball stemmer - first, it lookups word in stop-word file and then does it job.
|
|
The reason - to minimize possible 'noise'.
|
|
</ul>
|
|
|
|
<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>
|
|
Notice, that ranking functions supplied are just an examples and
|
|
doesn't belong to the tsearch2 core, you can
|
|
write your very own ranking function and/or combine additional
|
|
factors to fit your specific interest.
|
|
</p>
|
|
|
|
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>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 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>.
|
|
</dd>
|
|
<dt>
|
|
<tt>CREATE FUNCTION get_covers(vector TSVECTOR, query TSQUERY) RETURNS text</tt>
|
|
</dt>
|
|
<dd>
|
|
Returns <tt>extents</tt>, which are a shortest and non-nested sequences of words, which satisfy a query.
|
|
Extents (covers) used in <tt>rank_cd</tt> algorithm for fast calculation of proximity ranking.
|
|
In example below there are two extents - <tt><b>{1</b>...<b>}1</b> and <b>{2</b> ...<b>}2</b></tt>.
|
|
<pre>
|
|
=# select get_covers('1:1,2,10 2:4'::tsvector,'1& 2');
|
|
get_covers
|
|
----------------------
|
|
1 {1 1 {2 2 }1 1 }2
|
|
</pre>
|
|
</dd>
|
|
|
|
</dl>
|
|
|
|
<p>
|
|
Both of these (<tt>rank(), rank_cd()</tt>) 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, which could be combined using "|" ( 2|4) to
|
|
take into account several factors:
|
|
|
|
</p>
|
|
<ul>
|
|
<li><tt>0</tt> (the default) ignores document length.</li>
|
|
<li><tt>1</tt> divides the rank by the 1 + logarithm of the length </li>
|
|
<li><tt>2</tt> divides the rank by the length itself.</li>
|
|
<li><tt>4</tt> divides the rank by the mean harmonic distance between extents</li>
|
|
<li><tt>8</tt> divides the rank by the number of unique words in document</li>
|
|
<li><tt>16</tt> divides the rank by 1 + logarithm of the number of unique words in document
|
|
</li>
|
|
</ul>
|
|
|
|
<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><li><tt>HighlightAll</tt> --
|
|
boolean flag, if TRUE, than the whole document will be highlighted.
|
|
</li></ul>
|
|
Any unspecified options receive these defaults:
|
|
<pre>StartSel=<b>, StopSel=</b>, MaxWords=35, MinWords=15, ShortWord=3, HighlightAll=FALSE
|
|
</pre>
|
|
</dd></dl>
|
|
|
|
|
|
<h2><a name="indexes">Indexes</a></h2>
|
|
Tsearch2 supports indexed access to tsvector in order to further speedup FTS. Notice, indexes are not mandatory for FTS !
|
|
<ul>
|
|
<li> RD-Tree (Russian Doll Tree, matryoshka), based on GiST (Generalized Search Tree)
|
|
<pre>
|
|
=# create index fts_idx on apod using gist(fts);
|
|
</pre>
|
|
<li>GIN - Generalized Inverted Index
|
|
<pre>
|
|
=# create index fts_idx on apod using gin(fts);
|
|
</pre>
|
|
</ul>
|
|
<b>GiST</b> index is very good for online update, but is not as scalable as <b>GIN</b> index,
|
|
which, in turn, isn't good for updates. Both indexes support concurrency and recovery.
|
|
|
|
<h2><a name="tz">Thesaurus dictionary</a></h2>
|
|
|
|
<P>
|
|
Thesaurus - is a collection of words with included information about the relationships of words and phrases,
|
|
i.e., broader terms (BT), narrower terms (NT), preferred terms, non-preferred, related terms,etc.</p>
|
|
<p>Basically,thesaurus dictionary replaces all non-preferred terms by one preferred term and, optionally,
|
|
preserves them for indexing. Thesaurus used when indexing, so any changes in thesaurus require reindexing.
|
|
Tsearch2's <tt>thesaurus</tt> dictionary (TZ) is an extension of <tt>synonym</tt> dictionary
|
|
with <b>phrase</b> support. Thesaurus is a plain file of the following format:
|
|
<pre>
|
|
# this is a comment
|
|
sample word(s) : indexed word(s)
|
|
...............................
|
|
</pre>
|
|
<ul>
|
|
<li><strong>Colon</strong> (:) symbol used as a delimiter.</li>
|
|
<li>Use asterisk (<b>*</b>) at the beginning of <tt>indexed word</tt> to skip subdictionary.
|
|
It's still required, that <tt>sample words</tt> should be known.</li>
|
|
<li>thesaurus dictionary looks for the most longest match</li></ul>
|
|
<P>
|
|
TZ uses <strong>subdictionary</strong> (should be defined in tsearch2 configuration)
|
|
to normalize thesaurus text. It's possible to define only <strong>one dictionary</strong>.
|
|
Notice, that subdictionary produces an error, if it couldn't recognize word.
|
|
In that case, you should remove definition line with this word or teach subdictionary to know it.
|
|
</p>
|
|
<p>Stop-words recognized by subdictionary replaced by 'stop-word placeholder', i.e.,
|
|
important only their position.
|
|
To break possible ties thesaurus applies the last definition. For example, consider
|
|
thesaurus (with simple subdictionary) rules with pattern 'swsw'
|
|
('s' designates stop-word and 'w' - known word): </p>
|
|
<pre>
|
|
a one the two : swsw
|
|
the one a two : swsw2
|
|
</pre>
|
|
<p>Words 'a' and 'the' are stop-words defined in the configuration of a subdictionary.
|
|
Thesaurus considers texts 'the one the two' and 'that one then two' as equal and will use definition
|
|
'swsw2'.</p>
|
|
<p>As a normal dictionary, it should be assigned to the specific lexeme types.
|
|
Since TZ has a capability to recognize phrases it must remember its state and interact with parser.
|
|
TZ use these assignments to check if it should handle next word or stop accumulation.
|
|
Compiler of TZ should take care about proper configuration to avoid confusion.
|
|
For example, if TZ is assigned to handle only <tt>lword</tt> lexeme, then TZ definition like
|
|
' one 1:11' will not works, since lexeme type <tt>digit</tt> doesn't assigned to the TZ.</p>
|
|
|
|
<h3>Configuration</h3>
|
|
|
|
<dl><dt>tsearch2</dt><dd></dd></dl><p>tsearch2 comes with thesaurus template, which could be used to define new dictionary: </p>
|
|
<pre class="real">INSERT INTO pg_ts_dict
|
|
(SELECT 'tz_simple', dict_init,
|
|
'DictFile="/path/to/tz_simple.txt",'
|
|
'Dictionary="en_stem"',
|
|
dict_lexize
|
|
FROM pg_ts_dict
|
|
WHERE dict_name = 'thesaurus_template');
|
|
|
|
</pre>
|
|
<p>Here: </p>
|
|
<ul>
|
|
<li><tt>tz_simple</tt> - is the dictionary name</li>
|
|
<li><tt>DictFile="/path/to/tz_simple.txt"</tt> - is the location of thesaurus file</li>
|
|
<li><tt>Dictionary="en_stem"</tt> defines dictionary (snowball english stemmer) to use for thesaurus normalization. Notice, that <em>en_stem</em> dictionary has it's own configuration (stop-words, for example).</li>
|
|
</ul>
|
|
<p>Now, it's possible to use <tt>tz_simple</tt> in pg_ts_cfgmap, for example: </p>
|
|
<pre>
|
|
update pg_ts_cfgmap set dict_name='{tz_simple,en_stem}' where ts_name = 'default_russian' and
|
|
tok_alias in ('lhword', 'lword', 'lpart_hword');
|
|
</pre>
|
|
<h3>Examples</h3>
|
|
<p>tz_simple: </p>
|
|
<pre>
|
|
one : 1
|
|
two : 2
|
|
one two : 12
|
|
the one : 1
|
|
one 1 : 11
|
|
</pre>
|
|
<p>To see, how thesaurus works, one could use <tt>to_tsvector</tt>, <tt>to_tsquery</tt> or <tt>plainto_tsquery</tt> functions: </p><pre class="real">=# select plainto_tsquery('default_russian',' one day is oneday');
|
|
plainto_tsquery
|
|
------------------------
|
|
'1' & 'day' & 'oneday'
|
|
|
|
=# select plainto_tsquery('default_russian','one two day is oneday');
|
|
plainto_tsquery
|
|
-------------------------
|
|
'12' & 'day' & 'oneday'
|
|
|
|
=# select plainto_tsquery('default_russian','the one');
|
|
NOTICE: Thesaurus: word 'the' is recognized as stop-word, assign any stop-word (rule 3)
|
|
plainto_tsquery
|
|
-----------------
|
|
'1'
|
|
</pre>
|
|
|
|
Additional information about thesaurus dictionary is available from
|
|
<a href="http://www.sai.msu.su/~megera/wiki/Thesaurus_dictionary">Wiki</a> page.
|
|
</body></html>
|