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‎contrib/tsearch2/docs/tsearch-V2-intro.html

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@@ -427,9 +427,9 @@ <h3>INDEXING FIELDS IN A TABLE</h3>
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<p>We need to create the index on the column idxFTI. Keep in mind
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that the database will update the index when some action is taken.
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In this case we _need_ the index (The whole point of Full Text
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INDEXINGi ;-)), so don't worry about any indexing overhead. We will
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create an index based on the gist function. GiST is an index
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structure for Generalized Search Tree.</p>
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INDEXING ;-)), so don't worry about any indexing overhead. We will
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create an index based on the gist or gin function. GiST is an index
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structure for Generalized Search Tree, GIN is a inverted index (see <a href="tsearch2-ref.html#indexes">The tsearch2 Reference: Indexes</a>).</p>
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<pre>
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CREATE INDEX idxFTI_idx ON tblMessages USING gist(idxFTI);
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VACUUM FULL ANALYZE;

‎contrib/tsearch2/docs/tsearch2-guide.html

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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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<html>
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<head>
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<link type="text/css" rel="stylesheet" href="/~megera/postgres/gist/tsearch/tsearch.css">
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<title>tsearch2 guide</title>
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</head>
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<body>
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<h1 align=center>The tsearch2 Guide</h1>
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<p align=center>
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Brandon Craig Rhodes<br>30 June 2003
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<br>Updated to 8.2 release by Oleg Bartunov, October 2006</br>
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<p>
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This Guide introduces the reader to the PostgreSQL tsearch2 module,
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version&nbsp;2.
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More formal descriptions of the module's types and functions
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are provided in the <a href="tsearch2-ref.html">tsearch2 Reference</a>,
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which is a companion to this document.
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You can retrieve a beta copy of the tsearch2 module from the
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<a href="http://www.sai.msu.su/~megera/postgres/gist/">GiST for PostgreSQL</a>
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page &mdash; look under the section entitled <i>Development History</i>
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for the current version.
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<p>
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First we will examine the <tt>tsvector</tt> and <tt>tsquery</tt> types
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and how they are used to search documents;
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<hr>
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<h2>Table of Contents</h2>
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<blockquote>
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<a href="#intro">Introduction to FTS with tsearch2</a><br>
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<a href="#vectors_queries">Vectors and Queries</a><br>
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<a href="#simple_search">A Simple Search Engine</a><br>
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<a href="#weights">Ranking and Position Weights</a><br>
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<a href="#casting">Casting Vectors and Queries</a><br>
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<a href="#parsing_lexing">Parsing and Lexing</a><br>
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<a href="#ref">Additional information</a>
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</blockquote>
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<hr>
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<h2><a name="intro">Introduction to FTS with tsearch2</a></h2>
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The purpose of FTS is to
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find <b>documents</b>, which satisfy <b>query</b> and optionally return
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them in some <b>order</b>.
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Most common case: Find documents containing all query terms and return them in order
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of their similarity to the query. Document in database can be
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any text attribute, or combination of text attributes from one or many tables
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(using joins).
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Text search operators existed for years, in PostgreSQL they are
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<tt><b>~,~*, LIKE, ILIKE</b></tt>, but they lack linguistic support,
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tends to be slow and have no relevance ranking. The idea behind tsearch2 is
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is rather simple - preprocess document at index time to save time at search stage.
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Preprocessing includes
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<ul>
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<li>document parsing onto words
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<li>linguistic - normalize words to obtain lexemes
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<li>store document in optimized for searching way
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</ul>
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Tsearch2, in a nutshell, provides FTS operator (contains) for two new data types,
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which represent document and query - <tt>tsquery @@ tsvector</tt>.
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<P>
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<h2><a name=vectors_queries>Vectors and Queries</a></h2>
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<blockquote>
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on the <tt>tsvector</tt> column of a table,
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which implements a form of the Berkeley
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<a href="http://gist.cs.berkeley.edu/"><i>Generalized Search Tree</i></a>.
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Since PostgreSQL 8.2 tsearch2 supports <a href="http://www.sigaev.ru/gin/">Gin</a> index,
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which is an inverted index, commonly used in search engines. It adds scalability to tsearch2.
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</ul>
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Once your documents are indexed,
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performing a search involves:
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<pre>
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=# <b>SELECT to_tsquery('the')</b>
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NOTICE: Query contains only stopword(s) or doesn't contain lexeme(s), ignored
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NOTICE: Query contains only stopword(s) or doesn't contain lexem(s), ignored
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to_tsquery
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------------
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and has the feature that you can assign different weights
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to words from different sections of your document.
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The <tt>rank_cd()</tt> uses a recent technique for weighting results
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but does not allow different weight to be given
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to different sections of your document.
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and also allows different weight to be given
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to different sections of your document (since 8.2).
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<p>
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Both ranking functions allow you to specify,
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as an optional last argument,
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see the <a href="tsearch2-ref.html#ranking">section on ranking</a>
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in the Reference.
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<p>
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The <tt>rank()</tt> function offers more flexibility
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because it pays attention to the <i>weights</i>
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with which you have labelled lexeme positions.
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Currently tsearch2 supports four different weight labels:
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<tt>'D'</tt>, the default weight;
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and <tt>'A'</tt>, <tt>'B'</tt>, and <tt>'C'</tt>.
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are important <i>both</i> to PostgreSQL when it is interpreting a string,
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<i>and</i> to the <tt>tsvector</tt> conversion function.
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You may want to review section
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<a href="http://www.postgresql.org/docs/view.php?version=7.3&idoc=0&file=sql-syntax.html#SQL-SYNTAX-STRINGS">1.1.2.1,
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<a href="http://www.postgresql.org/docs/current/static/sql-syntax.html#SQL-SYNTAX-STRINGS">
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&ldquo;String Constants&rdquo;</a>
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in the PostgreSQL documentation before proceeding.
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<p>
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with the difference that the query parser recognizes as special
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the boolean operators that separate query words.
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<h2><a name="ref">Additional information</a></h2>
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More information about tsearch2 is available from
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<a href="http://www.sai.msu.su/~megera/postgres/gist/tsearch/V2">tsearch2</a> page.
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Also, it's worth to check
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<a href="http://www.sai.msu.su/~megera/wiki/Tsearch2">tsearch2 wiki</a> pages.
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</body>
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</html>
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