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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>12.3. Controlling Text Search</title><link rel="stylesheet" type="text/css" href="stylesheet.css" /><link rev="made" href="pgsql-docs@lists.postgresql.org" /><meta name="generator" content="DocBook XSL Stylesheets Vsnapshot" /><link rel="prev" href="textsearch-tables.html" title="12.2. Tables and Indexes" /><link rel="next" href="textsearch-features.html" title="12.4. Additional Features" /></head><body><div xmlns="http://www.w3.org/TR/xhtml1/transitional" class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="5" align="center">12.3. Controlling Text Search</th></tr><tr><td width="10%" align="left"><a accesskey="p" href="textsearch-tables.html" title="12.2. Tables and Indexes">Prev</a> </td><td width="10%" align="left"><a accesskey="u" href="textsearch.html" title="Chapter 12. Full Text Search">Up</a></td><th width="60%" align="center">Chapter 12. Full Text Search</th><td width="10%" align="right"><a accesskey="h" href="index.html" title="PostgreSQL 11.5 Documentation">Home</a></td><td width="10%" align="right"> <a accesskey="n" href="textsearch-features.html" title="12.4. Additional Features">Next</a></td></tr></table><hr></hr></div><div class="sect1" id="TEXTSEARCH-CONTROLS"><div class="titlepage"><div><div><h2 class="title" style="clear: both">12.3. Controlling Text Search</h2></div></div></div><div class="toc"><dl class="toc"><dt><span class="sect2"><a href="textsearch-controls.html#TEXTSEARCH-PARSING-DOCUMENTS">12.3.1. Parsing Documents</a></span></dt><dt><span class="sect2"><a href="textsearch-controls.html#TEXTSEARCH-PARSING-QUERIES">12.3.2. Parsing Queries</a></span></dt><dt><span class="sect2"><a href="textsearch-controls.html#TEXTSEARCH-RANKING">12.3.3. Ranking Search Results</a></span></dt><dt><span class="sect2"><a href="textsearch-controls.html#TEXTSEARCH-HEADLINE">12.3.4. Highlighting Results</a></span></dt></dl></div><p>
To implement full text searching there must be a function to create a
<code class="type">tsvector</code> from a document and a <code class="type">tsquery</code> from a
user query. Also, we need to return results in a useful order, so we need
a function that compares documents with respect to their relevance to
the query. It's also important to be able to display the results nicely.
<span class="productname">PostgreSQL</span> provides support for all of these
functions.
</p><div class="sect2" id="TEXTSEARCH-PARSING-DOCUMENTS"><div class="titlepage"><div><div><h3 class="title">12.3.1. Parsing Documents</h3></div></div></div><p>
<span class="productname">PostgreSQL</span> provides the
function <code class="function">to_tsvector</code> for converting a document to
the <code class="type">tsvector</code> data type.
</p><a id="id-1.5.11.6.3.3" class="indexterm"></a><pre class="synopsis">
to_tsvector([<span class="optional"> <em class="replaceable"><code>config</code></em> <code class="type">regconfig</code>, </span>] <em class="replaceable"><code>document</code></em> <code class="type">text</code>) returns <code class="type">tsvector</code>
</pre><p>
<code class="function">to_tsvector</code> parses a textual document into tokens,
reduces the tokens to lexemes, and returns a <code class="type">tsvector</code> which
lists the lexemes together with their positions in the document.
The document is processed according to the specified or default
text search configuration.
Here is a simple example:
</p><pre class="screen">
SELECT to_tsvector('english', 'a fat cat sat on a mat - it ate a fat rats');
to_tsvector
-----------------------------------------------------
'ate':9 'cat':3 'fat':2,11 'mat':7 'rat':12 'sat':4
</pre><p>
</p><p>
In the example above we see that the resulting <code class="type">tsvector</code> does not
contain the words <code class="literal">a</code>, <code class="literal">on</code>, or
<code class="literal">it</code>, the word <code class="literal">rats</code> became
<code class="literal">rat</code>, and the punctuation sign <code class="literal">-</code> was
ignored.
</p><p>
The <code class="function">to_tsvector</code> function internally calls a parser
which breaks the document text into tokens and assigns a type to
each token. For each token, a list of
dictionaries (<a class="xref" href="textsearch-dictionaries.html" title="12.6. Dictionaries">Section 12.6</a>) is consulted,
where the list can vary depending on the token type. The first dictionary
that <em class="firstterm">recognizes</em> the token emits one or more normalized
<em class="firstterm">lexemes</em> to represent the token. For example,
<code class="literal">rats</code> became <code class="literal">rat</code> because one of the
dictionaries recognized that the word <code class="literal">rats</code> is a plural
form of <code class="literal">rat</code>. Some words are recognized as
<em class="firstterm">stop words</em> (<a class="xref" href="textsearch-dictionaries.html#TEXTSEARCH-STOPWORDS" title="12.6.1. Stop Words">Section 12.6.1</a>), which
causes them to be ignored since they occur too frequently to be useful in
searching. In our example these are
<code class="literal">a</code>, <code class="literal">on</code>, and <code class="literal">it</code>.
If no dictionary in the list recognizes the token then it is also ignored.
In this example that happened to the punctuation sign <code class="literal">-</code>
because there are in fact no dictionaries assigned for its token type
(<code class="literal">Space symbols</code>), meaning space tokens will never be
indexed. The choices of parser, dictionaries and which types of tokens to
index are determined by the selected text search configuration (<a class="xref" href="textsearch-configuration.html" title="12.7. Configuration Example">Section 12.7</a>). It is possible to have
many different configurations in the same database, and predefined
configurations are available for various languages. In our example
we used the default configuration <code class="literal">english</code> for the
English language.
</p><p>
The function <code class="function">setweight</code> can be used to label the
entries of a <code class="type">tsvector</code> with a given <em class="firstterm">weight</em>,
where a weight is one of the letters <code class="literal">A</code>, <code class="literal">B</code>,
<code class="literal">C</code>, or <code class="literal">D</code>.
This is typically used to mark entries coming from
different parts of a document, such as title versus body. Later, this
information can be used for ranking of search results.
</p><p>
Because <code class="function">to_tsvector</code>(<code class="literal">NULL</code>) will
return <code class="literal">NULL</code>, it is recommended to use
<code class="function">coalesce</code> whenever a field might be null.
Here is the recommended method for creating
a <code class="type">tsvector</code> from a structured document:
</p><pre class="programlisting">
UPDATE tt SET ti =
setweight(to_tsvector(coalesce(title,'')), 'A') ||
setweight(to_tsvector(coalesce(keyword,'')), 'B') ||
setweight(to_tsvector(coalesce(abstract,'')), 'C') ||
setweight(to_tsvector(coalesce(body,'')), 'D');
</pre><p>
Here we have used <code class="function">setweight</code> to label the source
of each lexeme in the finished <code class="type">tsvector</code>, and then merged
the labeled <code class="type">tsvector</code> values using the <code class="type">tsvector</code>
concatenation operator <code class="literal">||</code>. (<a class="xref" href="textsearch-features.html#TEXTSEARCH-MANIPULATE-TSVECTOR" title="12.4.1. Manipulating Documents">Section 12.4.1</a> gives details about these
operations.)
</p></div><div class="sect2" id="TEXTSEARCH-PARSING-QUERIES"><div class="titlepage"><div><div><h3 class="title">12.3.2. Parsing Queries</h3></div></div></div><p>
<span class="productname">PostgreSQL</span> provides the
functions <code class="function">to_tsquery</code>,
<code class="function">plainto_tsquery</code>,
<code class="function">phraseto_tsquery</code> and
<code class="function">websearch_to_tsquery</code>
for converting a query to the <code class="type">tsquery</code> data type.
<code class="function">to_tsquery</code> offers access to more features
than either <code class="function">plainto_tsquery</code> or
<code class="function">phraseto_tsquery</code>, but it is less forgiving about its
input. <code class="function">websearch_to_tsquery</code> is a simplified version
of <code class="function">to_tsquery</code> with an alternative syntax, similar
to the one used by web search engines.
</p><a id="id-1.5.11.6.4.3" class="indexterm"></a><pre class="synopsis">
to_tsquery([<span class="optional"> <em class="replaceable"><code>config</code></em> <code class="type">regconfig</code>, </span>] <em class="replaceable"><code>querytext</code></em> <code class="type">text</code>) returns <code class="type">tsquery</code>
</pre><p>
<code class="function">to_tsquery</code> creates a <code class="type">tsquery</code> value from
<em class="replaceable"><code>querytext</code></em>, which must consist of single tokens
separated by the <code class="type">tsquery</code> operators <code class="literal">&</code> (AND),
<code class="literal">|</code> (OR), <code class="literal">!</code> (NOT), and
<code class="literal"><-></code> (FOLLOWED BY), possibly grouped
using parentheses. In other words, the input to
<code class="function">to_tsquery</code> must already follow the general rules for
<code class="type">tsquery</code> input, as described in <a class="xref" href="datatype-textsearch.html#DATATYPE-TSQUERY" title="8.11.2. tsquery">Section 8.11.2</a>. The difference is that while basic
<code class="type">tsquery</code> input takes the tokens at face value,
<code class="function">to_tsquery</code> normalizes each token into a lexeme using
the specified or default configuration, and discards any tokens that are
stop words according to the configuration. For example:
</p><pre class="screen">
SELECT to_tsquery('english', 'The & Fat & Rats');
to_tsquery
---------------
'fat' & 'rat'
</pre><p>
As in basic <code class="type">tsquery</code> input, weight(s) can be attached to each
lexeme to restrict it to match only <code class="type">tsvector</code> lexemes of those
weight(s). For example:
</p><pre class="screen">
SELECT to_tsquery('english', 'Fat | Rats:AB');
to_tsquery
------------------
'fat' | 'rat':AB
</pre><p>
Also, <code class="literal">*</code> can be attached to a lexeme to specify prefix matching:
</p><pre class="screen">
SELECT to_tsquery('supern:*A & star:A*B');
to_tsquery
--------------------------
'supern':*A & 'star':*AB
</pre><p>
Such a lexeme will match any word in a <code class="type">tsvector</code> that begins
with the given string.
</p><p>
<code class="function">to_tsquery</code> can also accept single-quoted
phrases. This is primarily useful when the configuration includes a
thesaurus dictionary that may trigger on such phrases.
In the example below, a thesaurus contains the rule <code class="literal">supernovae
stars : sn</code>:
</p><pre class="screen">
SELECT to_tsquery('''supernovae stars'' & !crab');
to_tsquery
---------------
'sn' & !'crab'
</pre><p>
Without quotes, <code class="function">to_tsquery</code> will generate a syntax
error for tokens that are not separated by an AND, OR, or FOLLOWED BY
operator.
</p><a id="id-1.5.11.6.4.7" class="indexterm"></a><pre class="synopsis">
plainto_tsquery([<span class="optional"> <em class="replaceable"><code>config</code></em> <code class="type">regconfig</code>, </span>] <em class="replaceable"><code>querytext</code></em> <code class="type">text</code>) returns <code class="type">tsquery</code>
</pre><p>
<code class="function">plainto_tsquery</code> transforms the unformatted text
<em class="replaceable"><code>querytext</code></em> to a <code class="type">tsquery</code> value.
The text is parsed and normalized much as for <code class="function">to_tsvector</code>,
then the <code class="literal">&</code> (AND) <code class="type">tsquery</code> operator is
inserted between surviving words.
</p><p>
Example:
</p><pre class="screen">
SELECT plainto_tsquery('english', 'The Fat Rats');
plainto_tsquery
-----------------
'fat' & 'rat'
</pre><p>
Note that <code class="function">plainto_tsquery</code> will not
recognize <code class="type">tsquery</code> operators, weight labels,
or prefix-match labels in its input:
</p><pre class="screen">
SELECT plainto_tsquery('english', 'The Fat & Rats:C');
plainto_tsquery
---------------------
'fat' & 'rat' & 'c'
</pre><p>
Here, all the input punctuation was discarded as being space symbols.
</p><a id="id-1.5.11.6.4.11" class="indexterm"></a><pre class="synopsis">
phraseto_tsquery([<span class="optional"> <em class="replaceable"><code>config</code></em> <code class="type">regconfig</code>, </span>] <em class="replaceable"><code>querytext</code></em> <code class="type">text</code>) returns <code class="type">tsquery</code>
</pre><p>
<code class="function">phraseto_tsquery</code> behaves much like
<code class="function">plainto_tsquery</code>, except that it inserts
the <code class="literal"><-></code> (FOLLOWED BY) operator between
surviving words instead of the <code class="literal">&</code> (AND) operator.
Also, stop words are not simply discarded, but are accounted for by
inserting <code class="literal"><<em class="replaceable"><code>N</code></em>></code> operators rather
than <code class="literal"><-></code> operators. This function is useful
when searching for exact lexeme sequences, since the FOLLOWED BY
operators check lexeme order not just the presence of all the lexemes.
</p><p>
Example:
</p><pre class="screen">
SELECT phraseto_tsquery('english', 'The Fat Rats');
phraseto_tsquery
------------------
'fat' <-> 'rat'
</pre><p>
Like <code class="function">plainto_tsquery</code>, the
<code class="function">phraseto_tsquery</code> function will not
recognize <code class="type">tsquery</code> operators, weight labels,
or prefix-match labels in its input:
</p><pre class="screen">
SELECT phraseto_tsquery('english', 'The Fat & Rats:C');
phraseto_tsquery
-----------------------------
'fat' <-> 'rat' <-> 'c'
</pre><p>
</p><pre class="synopsis">
websearch_to_tsquery([<span class="optional"> <em class="replaceable"><code>config</code></em> <code class="type">regconfig</code>, </span>] <em class="replaceable"><code>querytext</code></em> <code class="type">text</code>) returns <code class="type">tsquery</code>
</pre><p>
<code class="function">websearch_to_tsquery</code> creates a <code class="type">tsquery</code>
value from <em class="replaceable"><code>querytext</code></em> using an alternative
syntax in which simple unformatted text is a valid query.
Unlike <code class="function">plainto_tsquery</code>
and <code class="function">phraseto_tsquery</code>, it also recognizes certain
operators. Moreover, this function should never raise syntax errors,
which makes it possible to use raw user-supplied input for search.
The following syntax is supported:
</p><div class="itemizedlist"><ul class="itemizedlist compact" style="list-style-type: bullet; "><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">unquoted text</code>: text not inside quote marks will be
converted to terms separated by <code class="literal">&</code> operators, as
if processed by
<code class="function">plainto_tsquery</code>.
</p></li><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">"quoted text"</code>: text inside quote marks will be
converted to terms separated by <code class="literal"><-></code>
operators, as if processed by <code class="function">phraseto_tsquery</code>.
</p></li><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">OR</code>: logical or will be converted to
the <code class="literal">|</code> operator.
</p></li><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">-</code>: the logical not operator, converted to the
the <code class="literal">!</code> operator.
</p></li></ul></div><p>
</p><p>
Examples:
</p><pre class="screen">
SELECT websearch_to_tsquery('english', 'The fat rats');
websearch_to_tsquery
----------------------
'fat' & 'rat'
(1 row)
SELECT websearch_to_tsquery('english', '"supernovae stars" -crab');
websearch_to_tsquery
----------------------------------
'supernova' <-> 'star' & !'crab'
(1 row)
SELECT websearch_to_tsquery('english', '"sad cat" or "fat rat"');
websearch_to_tsquery
-----------------------------------
'sad' <-> 'cat' | 'fat' <-> 'rat'
(1 row)
SELECT websearch_to_tsquery('english', 'signal -"segmentation fault"');
websearch_to_tsquery
---------------------------------------
'signal' & !( 'segment' <-> 'fault' )
(1 row)
SELECT websearch_to_tsquery('english', '""" )( dummy \\ query <->');
websearch_to_tsquery
----------------------
'dummi' & 'queri'
(1 row)
</pre><p>
</p></div><div class="sect2" id="TEXTSEARCH-RANKING"><div class="titlepage"><div><div><h3 class="title">12.3.3. Ranking Search Results</h3></div></div></div><p>
Ranking attempts to measure how relevant documents are to a particular
query, so that when there are many matches the most relevant ones can be
shown first. <span class="productname">PostgreSQL</span> provides two
predefined ranking functions, which take into account lexical, proximity,
and structural information; that is, they consider how often the query
terms appear in the document, how close together the terms are in the
document, and how important is the part of the document where they occur.
However, the concept of relevancy is vague and very application-specific.
Different applications might require additional information for ranking,
e.g., document modification time. The built-in ranking functions are only
examples. You can write your own ranking functions and/or combine their
results with additional factors to fit your specific needs.
</p><p>
The two ranking functions currently available are:
</p><div class="variablelist"><dl class="variablelist"><dt><span class="term">
<a id="id-1.5.11.6.5.3.1.1.1.1" class="indexterm"></a>
<code class="literal">ts_rank([<span class="optional"> <em class="replaceable"><code>weights</code></em> <code class="type">float4[]</code>, </span>] <em class="replaceable"><code>vector</code></em> <code class="type">tsvector</code>, <em class="replaceable"><code>query</code></em> <code class="type">tsquery</code> [<span class="optional">, <em class="replaceable"><code>normalization</code></em> <code class="type">integer</code> </span>]) returns <code class="type">float4</code></code>
</span></dt><dd><p>
Ranks vectors based on the frequency of their matching lexemes.
</p></dd><dt><span class="term">
<a id="id-1.5.11.6.5.3.1.2.1.1" class="indexterm"></a>
<code class="literal">ts_rank_cd([<span class="optional"> <em class="replaceable"><code>weights</code></em> <code class="type">float4[]</code>, </span>] <em class="replaceable"><code>vector</code></em> <code class="type">tsvector</code>, <em class="replaceable"><code>query</code></em> <code class="type">tsquery</code> [<span class="optional">, <em class="replaceable"><code>normalization</code></em> <code class="type">integer</code> </span>]) returns <code class="type">float4</code></code>
</span></dt><dd><p>
This function computes the <em class="firstterm">cover density</em>
ranking for the given document vector and query, as described in
Clarke, Cormack, and Tudhope's "Relevance Ranking for One to Three
Term Queries" in the journal "Information Processing and Management",
1999. Cover density is similar to <code class="function">ts_rank</code> ranking
except that the proximity of matching lexemes to each other is
taken into consideration.
</p><p>
This function requires lexeme positional information to perform
its calculation. Therefore, it ignores any <span class="quote">“<span class="quote">stripped</span>”</span>
lexemes in the <code class="type">tsvector</code>. If there are no unstripped
lexemes in the input, the result will be zero. (See <a class="xref" href="textsearch-features.html#TEXTSEARCH-MANIPULATE-TSVECTOR" title="12.4.1. Manipulating Documents">Section 12.4.1</a> for more information
about the <code class="function">strip</code> function and positional information
in <code class="type">tsvector</code>s.)
</p></dd></dl></div><p>
</p><p>
For both these functions,
the optional <em class="replaceable"><code>weights</code></em>
argument offers the ability to weigh word instances more or less
heavily depending on how they are labeled. The weight arrays specify
how heavily to weigh each category of word, in the order:
</p><pre class="synopsis">
{D-weight, C-weight, B-weight, A-weight}
</pre><p>
If no <em class="replaceable"><code>weights</code></em> are provided,
then these defaults are used:
</p><pre class="programlisting">
{0.1, 0.2, 0.4, 1.0}
</pre><p>
Typically weights are used to mark words from special areas of the
document, like the title or an initial abstract, so they can be
treated with more or less importance than words in the document body.
</p><p>
Since a longer document has a greater chance of containing a query term
it is reasonable to take into account document size, e.g., a hundred-word
document with five instances of a search word is probably more relevant
than a thousand-word document with five instances. Both ranking functions
take an integer <em class="replaceable"><code>normalization</code></em> option that
specifies whether and how a document's length should impact its rank.
The integer option controls several behaviors, so it is a bit mask:
you can specify one or more behaviors using
<code class="literal">|</code> (for example, <code class="literal">2|4</code>).
</p><div class="itemizedlist"><ul class="itemizedlist compact" style="list-style-type: bullet; "><li class="listitem" style="list-style-type: disc"><p>
0 (the default) ignores the document length
</p></li><li class="listitem" style="list-style-type: disc"><p>
1 divides the rank by 1 + the logarithm of the document length
</p></li><li class="listitem" style="list-style-type: disc"><p>
2 divides the rank by the document length
</p></li><li class="listitem" style="list-style-type: disc"><p>
4 divides the rank by the mean harmonic distance between extents
(this is implemented only by <code class="function">ts_rank_cd</code>)
</p></li><li class="listitem" style="list-style-type: disc"><p>
8 divides the rank by the number of unique words in document
</p></li><li class="listitem" style="list-style-type: disc"><p>
16 divides the rank by 1 + the logarithm of the number
of unique words in document
</p></li><li class="listitem" style="list-style-type: disc"><p>
32 divides the rank by itself + 1
</p></li></ul></div><p>
If more than one flag bit is specified, the transformations are
applied in the order listed.
</p><p>
It is important to note that the ranking functions do not use any global
information, so it is impossible to produce a fair normalization to 1% or
100% as sometimes desired. Normalization option 32
(<code class="literal">rank/(rank+1)</code>) can be applied to scale all ranks
into the range zero to one, but of course this is just a cosmetic change;
it will not affect the ordering of the search results.
</p><p>
Here is an example that selects only the ten highest-ranked matches:
</p><pre class="screen">
SELECT title, ts_rank_cd(textsearch, query) AS rank
FROM apod, to_tsquery('neutrino|(dark & matter)') query
WHERE query @@ textsearch
ORDER BY rank DESC
LIMIT 10;
title | rank
-----------------------------------------------+----------
Neutrinos in the Sun | 3.1
The Sudbury Neutrino Detector | 2.4
A MACHO View of Galactic Dark Matter | 2.01317
Hot Gas and Dark Matter | 1.91171
The Virgo Cluster: Hot Plasma and Dark Matter | 1.90953
Rafting for Solar Neutrinos | 1.9
NGC 4650A: Strange Galaxy and Dark Matter | 1.85774
Hot Gas and Dark Matter | 1.6123
Ice Fishing for Cosmic Neutrinos | 1.6
Weak Lensing Distorts the Universe | 0.818218
</pre><p>
This is the same example using normalized ranking:
</p><pre class="screen">
SELECT title, ts_rank_cd(textsearch, query, 32 /* rank/(rank+1) */ ) AS rank
FROM apod, to_tsquery('neutrino|(dark & matter)') query
WHERE query @@ textsearch
ORDER BY rank DESC
LIMIT 10;
title | rank
-----------------------------------------------+-------------------
Neutrinos in the Sun | 0.756097569485493
The Sudbury Neutrino Detector | 0.705882361190954
A MACHO View of Galactic Dark Matter | 0.668123210574724
Hot Gas and Dark Matter | 0.65655958650282
The Virgo Cluster: Hot Plasma and Dark Matter | 0.656301290640973
Rafting for Solar Neutrinos | 0.655172410958162
NGC 4650A: Strange Galaxy and Dark Matter | 0.650072921219637
Hot Gas and Dark Matter | 0.617195790024749
Ice Fishing for Cosmic Neutrinos | 0.615384618911517
Weak Lensing Distorts the Universe | 0.450010798361481
</pre><p>
</p><p>
Ranking can be expensive since it requires consulting the
<code class="type">tsvector</code> of each matching document, which can be I/O bound and
therefore slow. Unfortunately, it is almost impossible to avoid since
practical queries often result in large numbers of matches.
</p></div><div class="sect2" id="TEXTSEARCH-HEADLINE"><div class="titlepage"><div><div><h3 class="title">12.3.4. Highlighting Results</h3></div></div></div><p>
To present search results it is ideal to show a part of each document and
how it is related to the query. Usually, search engines show fragments of
the document with marked search terms. <span class="productname">PostgreSQL</span>
provides a function <code class="function">ts_headline</code> that
implements this functionality.
</p><a id="id-1.5.11.6.6.3" class="indexterm"></a><pre class="synopsis">
ts_headline([<span class="optional"> <em class="replaceable"><code>config</code></em> <code class="type">regconfig</code>, </span>] <em class="replaceable"><code>document</code></em> <code class="type">text</code>, <em class="replaceable"><code>query</code></em> <code class="type">tsquery</code> [<span class="optional">, <em class="replaceable"><code>options</code></em> <code class="type">text</code> </span>]) returns <code class="type">text</code>
</pre><p>
<code class="function">ts_headline</code> accepts a document along
with a query, and returns an excerpt from
the document in which terms from the query are highlighted. The
configuration to be used to parse the document can be specified by
<em class="replaceable"><code>config</code></em>; if <em class="replaceable"><code>config</code></em>
is omitted, the
<code class="varname">default_text_search_config</code> configuration is used.
</p><p>
If an <em class="replaceable"><code>options</code></em> string is specified it must
consist of a comma-separated list of one or more
<em class="replaceable"><code>option</code></em><code class="literal">=</code><em class="replaceable"><code>value</code></em> pairs.
The available options are:
</p><div class="itemizedlist"><ul class="itemizedlist compact" style="list-style-type: bullet; "><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">StartSel</code>, <code class="literal">StopSel</code>: the strings with
which to delimit query words appearing in the document, to distinguish
them from other excerpted words. You must double-quote these strings
if they contain spaces or commas.
</p></li><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">MaxWords</code>, <code class="literal">MinWords</code>: these numbers
determine the longest and shortest headlines to output.
</p></li><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">ShortWord</code>: words of this length or less will be
dropped at the start and end of a headline. The default
value of three eliminates common English articles.
</p></li><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">HighlightAll</code>: Boolean flag; if
<code class="literal">true</code> the whole document will be used as the
headline, ignoring the preceding three parameters.
</p></li><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">MaxFragments</code>: maximum number of text excerpts
or fragments to display. The default value of zero selects a
non-fragment-oriented headline generation method. A value greater than
zero selects fragment-based headline generation. This method
finds text fragments with as many query words as possible and
stretches those fragments around the query words. As a result
query words are close to the middle of each fragment and have words on
each side. Each fragment will be of at most <code class="literal">MaxWords</code> and
words of length <code class="literal">ShortWord</code> or less are dropped at the start
and end of each fragment. If not all query words are found in the
document, then a single fragment of the first <code class="literal">MinWords</code>
in the document will be displayed.
</p></li><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">FragmentDelimiter</code>: When more than one fragment is
displayed, the fragments will be separated by this string.
</p></li></ul></div><p>
These option names are recognized case-insensitively.
Any unspecified options receive these defaults:
</p><pre class="programlisting">
StartSel=<b>, StopSel=</b>,
MaxWords=35, MinWords=15, ShortWord=3, HighlightAll=FALSE,
MaxFragments=0, FragmentDelimiter=" ... "
</pre><p>
</p><p>
For example:
</p><pre class="screen">
SELECT ts_headline('english',
'The most common type of search
is to find all documents containing given query terms
and return them in order of their similarity to the
query.',
to_tsquery('query & similarity'));
ts_headline
------------------------------------------------------------
containing given <b>query</b> terms
and return them in order of their <b>similarity</b> to the
<b>query</b>.
SELECT ts_headline('english',
'The most common type of search
is to find all documents containing given query terms
and return them in order of their similarity to the
query.',
to_tsquery('query & similarity'),
'StartSel = <, StopSel = >');
ts_headline
-------------------------------------------------------
containing given <query> terms
and return them in order of their <similarity> to the
<query>.
</pre><p>
</p><p>
<code class="function">ts_headline</code> uses the original document, not a
<code class="type">tsvector</code> summary, so it can be slow and should be used with
care.
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