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><DIV
CLASS="SECT1"
><H1
CLASS="SECT1"
><A
NAME="TEXTSEARCH-CONTROLS"
>12.3. Controlling Text Search</A
></H1
><P
> To implement full text searching there must be a function to create a
<TT
CLASS="TYPE"
>tsvector</TT
> from a document and a <TT
CLASS="TYPE"
>tsquery</TT
> 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"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-PARSING-DOCUMENTS"
>12.3.1. Parsing Documents</A
></H2
><P
> <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> provides the
function <CODE
CLASS="FUNCTION"
>to_tsvector</CODE
> for converting a document to
the <TT
CLASS="TYPE"
>tsvector</TT
> data type.
</P
><A
NAME="AEN18180"
></A
><PRE
CLASS="SYNOPSIS"
> to_tsvector([<SPAN
CLASS="OPTIONAL"
> <TT
CLASS="REPLACEABLE"
><I
>config</I
></TT
> <TT
CLASS="TYPE"
>regconfig</TT
>, </SPAN
>] <TT
CLASS="REPLACEABLE"
><I
>document</I
></TT
> <TT
CLASS="TYPE"
>text</TT
>) returns <TT
CLASS="TYPE"
>tsvector</TT
>
</PRE
><P
> <CODE
CLASS="FUNCTION"
>to_tsvector</CODE
> parses a textual document into tokens,
reduces the tokens to lexemes, and returns a <TT
CLASS="TYPE"
>tsvector</TT
> 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="PROGRAMLISTING"
>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 <TT
CLASS="TYPE"
>tsvector</TT
> does not
contain the words <TT
CLASS="LITERAL"
>a</TT
>, <TT
CLASS="LITERAL"
>on</TT
>, or
<TT
CLASS="LITERAL"
>it</TT
>, the word <TT
CLASS="LITERAL"
>rats</TT
> became
<TT
CLASS="LITERAL"
>rat</TT
>, and the punctuation sign <TT
CLASS="LITERAL"
>-</TT
> 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
HREF="textsearch-dictionaries.html"
>Section 12.6</A
>) is consulted,
where the list can vary depending on the token type. The first dictionary
that <I
CLASS="FIRSTTERM"
>recognizes</I
> the token emits one or more normalized
<I
CLASS="FIRSTTERM"
>lexemes</I
> to represent the token. For example,
<TT
CLASS="LITERAL"
>rats</TT
> became <TT
CLASS="LITERAL"
>rat</TT
> because one of the
dictionaries recognized that the word <TT
CLASS="LITERAL"
>rats</TT
> is a plural
form of <TT
CLASS="LITERAL"
>rat</TT
>. Some words are recognized as
<I
CLASS="FIRSTTERM"
>stop words</I
> (<A
HREF="textsearch-dictionaries.html#TEXTSEARCH-STOPWORDS"
>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
<TT
CLASS="LITERAL"
>a</TT
>, <TT
CLASS="LITERAL"
>on</TT
>, and <TT
CLASS="LITERAL"
>it</TT
>.
If no dictionary in the list recognizes the token then it is also ignored.
In this example that happened to the punctuation sign <TT
CLASS="LITERAL"
>-</TT
>
because there are in fact no dictionaries assigned for its token type
(<TT
CLASS="LITERAL"
>Space symbols</TT
>), 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
HREF="textsearch-configuration.html"
>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 <TT
CLASS="LITERAL"
>english</TT
> for the
English language.
</P
><P
> The function <CODE
CLASS="FUNCTION"
>setweight</CODE
> can be used to label the
entries of a <TT
CLASS="TYPE"
>tsvector</TT
> with a given <I
CLASS="FIRSTTERM"
>weight</I
>,
where a weight is one of the letters <TT
CLASS="LITERAL"
>A</TT
>, <TT
CLASS="LITERAL"
>B</TT
>,
<TT
CLASS="LITERAL"
>C</TT
>, or <TT
CLASS="LITERAL"
>D</TT
>.
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
>(<TT
CLASS="LITERAL"
>NULL</TT
>) will
return <TT
CLASS="LITERAL"
>NULL</TT
>, it is recommended to use
<CODE
CLASS="FUNCTION"
>coalesce</CODE
> whenever a field might be null.
Here is the recommended method for creating
a <TT
CLASS="TYPE"
>tsvector</TT
> 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 <TT
CLASS="TYPE"
>tsvector</TT
>, and then merged
the labeled <TT
CLASS="TYPE"
>tsvector</TT
> values using the <TT
CLASS="TYPE"
>tsvector</TT
>
concatenation operator <TT
CLASS="LITERAL"
>||</TT
>. (<A
HREF="textsearch-features.html#TEXTSEARCH-MANIPULATE-TSVECTOR"
>Section 12.4.1</A
> gives details about these
operations.)
</P
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-PARSING-QUERIES"
>12.3.2. Parsing Queries</A
></H2
><P
> <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> provides the
functions <CODE
CLASS="FUNCTION"
>to_tsquery</CODE
> and
<CODE
CLASS="FUNCTION"
>plainto_tsquery</CODE
> for converting a query to
the <TT
CLASS="TYPE"
>tsquery</TT
> data type. <CODE
CLASS="FUNCTION"
>to_tsquery</CODE
>
offers access to more features than <CODE
CLASS="FUNCTION"
>plainto_tsquery</CODE
>,
but is less forgiving about its input.
</P
><A
NAME="AEN18249"
></A
><PRE
CLASS="SYNOPSIS"
> to_tsquery([<SPAN
CLASS="OPTIONAL"
> <TT
CLASS="REPLACEABLE"
><I
>config</I
></TT
> <TT
CLASS="TYPE"
>regconfig</TT
>, </SPAN
>] <TT
CLASS="REPLACEABLE"
><I
>querytext</I
></TT
> <TT
CLASS="TYPE"
>text</TT
>) returns <TT
CLASS="TYPE"
>tsquery</TT
>
</PRE
><P
> <CODE
CLASS="FUNCTION"
>to_tsquery</CODE
> creates a <TT
CLASS="TYPE"
>tsquery</TT
> value from
<TT
CLASS="REPLACEABLE"
><I
>querytext</I
></TT
>, which must consist of single tokens
separated by the Boolean operators <TT
CLASS="LITERAL"
>&</TT
> (AND),
<TT
CLASS="LITERAL"
>|</TT
> (OR) and <TT
CLASS="LITERAL"
>!</TT
> (NOT). These operators
can be grouped using parentheses. In other words, the input to
<CODE
CLASS="FUNCTION"
>to_tsquery</CODE
> must already follow the general rules for
<TT
CLASS="TYPE"
>tsquery</TT
> input, as described in <A
HREF="datatype-textsearch.html"
>Section 8.11</A
>. The difference is that while basic
<TT
CLASS="TYPE"
>tsquery</TT
> input takes the tokens at face value,
<CODE
CLASS="FUNCTION"
>to_tsquery</CODE
> normalizes each token to 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="PROGRAMLISTING"
>SELECT to_tsquery('english', 'The & Fat & Rats');
to_tsquery
---------------
'fat' & 'rat'</PRE
><P>
As in basic <TT
CLASS="TYPE"
>tsquery</TT
> input, weight(s) can be attached to each
lexeme to restrict it to match only <TT
CLASS="TYPE"
>tsvector</TT
> lexemes of those
weight(s). For example:
</P><PRE
CLASS="PROGRAMLISTING"
>SELECT to_tsquery('english', 'Fat | Rats:AB');
to_tsquery
------------------
'fat' | 'rat':AB</PRE
><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 <TT
CLASS="LITERAL"
>supernovae
stars : sn</TT
>:
</P><PRE
CLASS="PROGRAMLISTING"
>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 operator.
</P
><A
NAME="AEN18278"
></A
><PRE
CLASS="SYNOPSIS"
> plainto_tsquery([<SPAN
CLASS="OPTIONAL"
> <TT
CLASS="REPLACEABLE"
><I
>config</I
></TT
> <TT
CLASS="TYPE"
>regconfig</TT
>, </SPAN
>] <TT
CLASS="REPLACEABLE"
><I
>querytext</I
></TT
> <TT
CLASS="TYPE"
>text</TT
>) returns <TT
CLASS="TYPE"
>tsquery</TT
>
</PRE
><P
> <CODE
CLASS="FUNCTION"
>plainto_tsquery</CODE
> transforms unformatted text
<TT
CLASS="REPLACEABLE"
><I
>querytext</I
></TT
> to <TT
CLASS="TYPE"
>tsquery</TT
>.
The text is parsed and normalized much as for <CODE
CLASS="FUNCTION"
>to_tsvector</CODE
>,
then the <TT
CLASS="LITERAL"
>&</TT
> (AND) Boolean operator is inserted
between surviving words.
</P
><P
> Example:
</P><PRE
CLASS="PROGRAMLISTING"
> SELECT plainto_tsquery('english', 'The Fat Rats');
plainto_tsquery
-----------------
'fat' & 'rat'</PRE
><P>
Note that <CODE
CLASS="FUNCTION"
>plainto_tsquery</CODE
> cannot
recognize either Boolean operators or weight labels in its input:
</P><PRE
CLASS="PROGRAMLISTING"
>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
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-RANKING"
>12.3.3. Ranking Search Results</A
></H2
><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
></P
></P><DIV
CLASS="VARIABLELIST"
><DL
><DT
><PRE
CLASS="SYNOPSIS"
> ts_rank([<SPAN
CLASS="OPTIONAL"
> <TT
CLASS="REPLACEABLE"
><I
>weights</I
></TT
> <TT
CLASS="TYPE"
>float4[]</TT
>, </SPAN
>] <TT
CLASS="REPLACEABLE"
><I
>vector</I
></TT
> <TT
CLASS="TYPE"
>tsvector</TT
>, <TT
CLASS="REPLACEABLE"
><I
>query</I
></TT
> <TT
CLASS="TYPE"
>tsquery</TT
> [<SPAN
CLASS="OPTIONAL"
>, <TT
CLASS="REPLACEABLE"
><I
>normalization</I
></TT
> <TT
CLASS="TYPE"
>integer</TT
> </SPAN
>]) returns <TT
CLASS="TYPE"
>float4</TT
>
</PRE
></DT
><DD
><P
> Standard ranking function.
</P
></DD
><DT
><PRE
CLASS="SYNOPSIS"
> ts_rank_cd([<SPAN
CLASS="OPTIONAL"
> <TT
CLASS="REPLACEABLE"
><I
>weights</I
></TT
> <TT
CLASS="TYPE"
>float4[]</TT
>, </SPAN
>] <TT
CLASS="REPLACEABLE"
><I
>vector</I
></TT
> <TT
CLASS="TYPE"
>tsvector</TT
>, <TT
CLASS="REPLACEABLE"
><I
>query</I
></TT
> <TT
CLASS="TYPE"
>tsquery</TT
> [<SPAN
CLASS="OPTIONAL"
>, <TT
CLASS="REPLACEABLE"
><I
>normalization</I
></TT
> <TT
CLASS="TYPE"
>integer</TT
> </SPAN
>]) returns <TT
CLASS="TYPE"
>float4</TT
>
</PRE
></DT
><DD
><P
> This function computes the <I
CLASS="FIRSTTERM"
>cover density</I
>
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.
</P
><P
> This function requires positional information in its input.
Therefore it will not work on <SPAN
CLASS="QUOTE"
>"stripped"</SPAN
> <TT
CLASS="TYPE"
>tsvector</TT
>
values — it will always return zero.
</P
></DD
></DL
></DIV
><P>
</P
><P
> For both these functions,
the optional <TT
CLASS="REPLACEABLE"
><I
>weights</I
></TT
>
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="PROGRAMLISTING"
>{D-weight, C-weight, B-weight, A-weight}</PRE
><P>
If no <TT
CLASS="REPLACEABLE"
><I
>weights</I
></TT
> 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 that they can be
treated as more or less important 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 <TT
CLASS="REPLACEABLE"
><I
>normalization</I
></TT
> 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
<TT
CLASS="LITERAL"
>|</TT
> (for example, <TT
CLASS="LITERAL"
>2|4</TT
>).
<P
></P
></P><UL
COMPACT="COMPACT"
><LI
STYLE="list-style-type: disc"
><P
> 0 (the default) ignores the document length
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> 1 divides the rank by 1 + the logarithm of the document length
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> 2 divides the rank by the document length
</P
></LI
><LI
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
STYLE="list-style-type: disc"
><P
> 8 divides the rank by the number of unique words in document
</P
></LI
><LI
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
STYLE="list-style-type: disc"
><P
> 32 divides the rank by itself + 1
</P
></LI
></UL
><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
(<TT
CLASS="LITERAL"
>rank/(rank+1)</TT
>) 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="PROGRAMLISTING"
>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="PROGRAMLISTING"
>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
<TT
CLASS="TYPE"
>tsvector</TT
> 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"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-HEADLINE"
>12.3.4. Highlighting Results</A
></H2
><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
NAME="AEN18380"
></A
><PRE
CLASS="SYNOPSIS"
> ts_headline([<SPAN
CLASS="OPTIONAL"
> <TT
CLASS="REPLACEABLE"
><I
>config</I
></TT
> <TT
CLASS="TYPE"
>regconfig</TT
>, </SPAN
>] <TT
CLASS="REPLACEABLE"
><I
>document</I
></TT
> <TT
CLASS="TYPE"
>text</TT
>, <TT
CLASS="REPLACEABLE"
><I
>query</I
></TT
> <TT
CLASS="TYPE"
>tsquery</TT
> [<SPAN
CLASS="OPTIONAL"
>, <TT
CLASS="REPLACEABLE"
><I
>options</I
></TT
> <TT
CLASS="TYPE"
>text</TT
> </SPAN
>]) returns <TT
CLASS="TYPE"
>text</TT
>
</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
<TT
CLASS="REPLACEABLE"
><I
>config</I
></TT
>; if <TT
CLASS="REPLACEABLE"
><I
>config</I
></TT
>
is omitted, the
<TT
CLASS="VARNAME"
>default_text_search_config</TT
> configuration is used.
</P
><P
> If an <TT
CLASS="REPLACEABLE"
><I
>options</I
></TT
> string is specified it must
consist of a comma-separated list of one or more
<TT
CLASS="REPLACEABLE"
><I
>option</I
></TT
><TT
CLASS="LITERAL"
>=</TT
><TT
CLASS="REPLACEABLE"
><I
>value</I
></TT
> pairs.
The available options are:
<P
></P
></P><UL
COMPACT="COMPACT"
><LI
STYLE="list-style-type: disc"
><P
> <TT
CLASS="LITERAL"
>StartSel</TT
>, <TT
CLASS="LITERAL"
>StopSel</TT
>: the strings with which
query words appearing in the document should be delimited to distinguish
them from other excerpted words.
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> <TT
CLASS="LITERAL"
>MaxWords</TT
>, <TT
CLASS="LITERAL"
>MinWords</TT
>: these numbers
determine the longest and shortest headlines to output.
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> <TT
CLASS="LITERAL"
>ShortWord</TT
>: words of this length or less will be
dropped at the start and end of a headline. The default
value of three eliminates the English articles.
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> <TT
CLASS="LITERAL"
>HighlightAll</TT
>: Boolean flag; if
<TT
CLASS="LITERAL"
>true</TT
> the whole document will be highlighted.
</P
></LI
></UL
><P>
Any unspecified options receive these defaults:
</P><PRE
CLASS="PROGRAMLISTING"
>StartSel=<b>, StopSel=</b>, MaxWords=35, MinWords=15, ShortWord=3, HighlightAll=FALSE</PRE
><P>
</P
><P
> For example:
</P><PRE
CLASS="PROGRAMLISTING"
>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
------------------------------------------------------------
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
-------------------------------------------------------
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
<TT
CLASS="TYPE"
>tsvector</TT
> summary, so it can be slow and should be used with
care. A typical mistake is to call <CODE
CLASS="FUNCTION"
>ts_headline</CODE
> for
<SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>every</I
></SPAN
> matching document when only ten documents are
to be shown. <ACRONYM
CLASS="ACRONYM"
>SQL</ACRONYM
> subqueries can help; here is an
example:
</P><PRE
CLASS="PROGRAMLISTING"
>SELECT id, ts_headline(body, q), rank
FROM (SELECT id, body, q, ts_rank_cd(ti, q) AS rank
FROM apod, to_tsquery('stars') q
WHERE ti @@ q
ORDER BY rank DESC LIMIT 10) AS foo;</PRE
><P>
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distrib
>
Mandriva
>
2008.1
>
x86_64
>
media
>
main-testing
>
by-pkgid
>
bab02a23fa9f3df8d66a9a3231b50245
>
files
>
841
