mirror of
https://git.postgresql.org/git/postgresql.git
synced 2024-12-27 08:39:28 +08:00
458 lines
18 KiB
HTML
458 lines
18 KiB
HTML
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"><html><head>
|
|
<link type="text/css" rel="stylesheet" href="tsearch2-ref_files/tsearch.txt"><title>tsearch2 reference</title></head>
|
|
|
|
<body>
|
|
<h1 align="center">The tsearch2 Reference</h1>
|
|
|
|
<p align="center">
|
|
Brandon Craig Rhodes<br>30 June 2003 (edited by Oleg Bartunov, 2 Aug 2003).
|
|
</p><p>
|
|
This Reference documents the user types and functions
|
|
of the tsearch2 module for PostgreSQL.
|
|
An introduction to the module is provided
|
|
by the <a href="http://www.sai.msu.su/%7Emegera/postgres/gist/tsearch/V2/docs/tsearch2-guide.html">tsearch2 Guide</a>,
|
|
a companion document to this one.
|
|
You can retrieve a beta copy of the tsearch2 module from the
|
|
<a href="http://www.sai.msu.su/%7Emegera/postgres/gist/">GiST for PostgreSQL</a>
|
|
page -- look under the section entitled <i>Development History</i>
|
|
for the current version.
|
|
|
|
</p><h2><a name="vq">Vectors and Queries</a></h2>
|
|
|
|
<a name="vq">Vectors and queries both store lexemes,
|
|
but for different purposes.
|
|
A <tt>tsvector</tt> stores the lexemes
|
|
of the words that are parsed out of a document,
|
|
and can also remember the position of each word.
|
|
A <tt>tsquery</tt> specifies a boolean condition among lexemes.
|
|
</a><p>
|
|
<a name="vq">Any of the following functions with a <tt><i>configuration</i></tt> argument
|
|
can use either an integer <tt>id</tt> or textual <tt>ts_name</tt>
|
|
to select a configuration;
|
|
if the option is omitted, then the current configuration is used.
|
|
For more information on the current configuration,
|
|
read the next section on Configurations.
|
|
|
|
</a></p><h3><a name="vq">Vector Operations</a></h3>
|
|
|
|
<dl><dt>
|
|
<a name="vq"> <tt>to_tsvector( <em>[</em><i>configuration</i>,<em>]</em>
|
|
<i>document</i> TEXT) RETURNS tsvector</tt>
|
|
</a></dt><dd>
|
|
<a name="vq"> Parses a document into tokens,
|
|
reduces the tokens to lexemes,
|
|
and returns a <tt>tsvector</tt> which lists the lexemes
|
|
together with their positions in the document.
|
|
For the best description of this process,
|
|
see the section on </a><a href="http://www.sai.msu.su/%7Emegera/postgres/gist/tsearch/V2/docs/tsearch2-guide.html#ps">Parsing and Stemming</a>
|
|
in the accompanying tsearch2 Guide.
|
|
</dd><dt>
|
|
<tt>strip(<i>vector</i> tsvector) RETURNS tsvector</tt>
|
|
</dt><dd>
|
|
Return a vector which lists the same lexemes
|
|
as the given <tt><i>vector</i></tt>,
|
|
but which lacks any information
|
|
about where in the document each lexeme appeared.
|
|
While the returned vector is thus useless for relevance ranking,
|
|
it will usually be much smaller.
|
|
</dd><dt>
|
|
<tt>setweight(<i>vector</i> tsvector, <i>letter</i>) RETURNS tsvector</tt>
|
|
</dt><dd>
|
|
This function returns a copy of the input vector
|
|
in which every location has been labelled
|
|
with either the <tt><i>letter</i></tt>
|
|
<tt>'A'</tt>, <tt>'B'</tt>, or <tt>'C'</tt>,
|
|
or the default label <tt>'D'</tt>
|
|
(which is the default with which new vectors are created,
|
|
and as such is usually not displayed).
|
|
These labels are retained when vectors are concatenated,
|
|
allowing words from different parts of a document
|
|
to be weighted differently by ranking functions.
|
|
</dd><dt>
|
|
<tt><i>vector1</i> || <i>vector2</i></tt>
|
|
</dt><dt class="br">
|
|
<tt>concat(<i>vector1</i> tsvector, <i>vector2</i> tsvector)
|
|
RETURNS tsvector</tt>
|
|
</dt><dd>
|
|
Returns a vector which combines the lexemes and position information
|
|
in the two vectors given as arguments.
|
|
Position weight labels (described in the previous paragraph)
|
|
are retained intact during the concatenation.
|
|
This has at least two uses.
|
|
First,
|
|
if some sections of your document
|
|
need be parsed with different configurations than others,
|
|
you can parse them separately
|
|
and concatenate the resulting vectors into one.
|
|
Second,
|
|
you can weight words from some sections of you document
|
|
more heavily than those from others by:
|
|
parsing the sections into separate vectors;
|
|
assigning the vectors different position labels
|
|
with the <tt>setweight()</tt> function;
|
|
concatenating them into a single vector;
|
|
and then providing a <tt><i>weights</i></tt> argument
|
|
to the <tt>rank()</tt> function
|
|
that assigns different weights to positions with different labels.
|
|
</dd><dt>
|
|
<tt>tsvector_size(<i>vector</i> tsvector) RETURNS INT4</tt>
|
|
</dt><dd>
|
|
Returns the number of lexemes stored in the vector.
|
|
</dd><dt>
|
|
<tt><i>text</i>::tsvector RETURNS tsvector</tt>
|
|
</dt><dd>
|
|
Directly casting text to a <tt>tsvector</tt>
|
|
allows you to directly inject lexemes into a vector,
|
|
with whatever positions and position weights you choose to specify.
|
|
The <tt><i>text</i></tt> should be formatted
|
|
like the vector would be printed by the output of a <tt>SELECT</tt>.
|
|
See the <a href="http://www.sai.msu.su/%7Emegera/postgres/gist/tsearch/V2/docs/tsearch2-guide.html#casting">Casting</a>
|
|
section in the Guide for details.
|
|
</dd></dl>
|
|
|
|
<h3>Query Operations</h3>
|
|
|
|
<dl><dt>
|
|
<tt>to_tsquery( <em>[</em><i>configuration</i>,<em>]</em>
|
|
<i>querytext</i> text) RETURNS tsvector</tt>
|
|
</dt><dd>
|
|
Parses a query,
|
|
which should be single words separated by the boolean operators
|
|
"<tt>&</tt>" and,
|
|
"<tt>|</tt>" or,
|
|
and "<tt>!</tt>" not,
|
|
which can be grouped using parenthesis.
|
|
Each word is reduced to a lexeme using the current
|
|
or specified configuration.
|
|
|
|
</dd><dt>
|
|
<tt>querytree(<i>query</i> tsquery) RETURNS text</tt>
|
|
</dt><dd>
|
|
This might return a textual representation of the given query.
|
|
</dd><dt>
|
|
<tt><i>text</i>::tsquery RETURNS tsquery</tt>
|
|
</dt><dd>
|
|
Directly casting text to a <tt>tsquery</tt>
|
|
allows you to directly inject lexemes into a query,
|
|
with whatever positions and position weight flags you choose to specify.
|
|
The <tt><i>text</i></tt> should be formatted
|
|
like the query would be printed by the output of a <tt>SELECT</tt>.
|
|
See the <a href="http://www.sai.msu.su/%7Emegera/postgres/gist/tsearch/V2/docs/tsearch2-guide.html#casting">Casting</a>
|
|
section in the Guide for details.
|
|
</dd></dl>
|
|
|
|
<h2><a name="configurations">Configurations</a></h2>
|
|
|
|
A configuration specifies all of the equipment necessary
|
|
to transform a document into a <tt>tsvector</tt>:
|
|
the parser that breaks its text into tokens,
|
|
and the dictionaries which then transform each token into a lexeme.
|
|
Every call to <tt>to_tsvector()</tt> (described above)
|
|
uses a configuration to perform its processing.
|
|
Three configurations come with tsearch2:
|
|
|
|
<ul>
|
|
<li><b>default</b> -- Indexes words and numbers,
|
|
using the <i>en_stem</i> English Snowball stemmer for Latin-alphabet words
|
|
and the <i>simple</i> dictionary for all others.
|
|
</li><li><b>default_russian</b> -- Indexes words and numbers,
|
|
using the <i>en_stem</i> English Snowball stemmer for Latin-alphabet words
|
|
and the <i>ru_stem</i> Russian Snowball dictionary for all others.
|
|
</li><li><b>simple</b> -- Processes both words and numbers
|
|
with the <i>simple</i> dictionary,
|
|
which neither discards any stop words nor alters them.
|
|
</li></ul>
|
|
|
|
The tsearch2 modules initially chooses your current configuration
|
|
by looking for your current locale in the <tt>locale</tt> field
|
|
of the <tt>pg_ts_cfg</tt> table described below.
|
|
You can manipulate the current configuration yourself with these functions:
|
|
|
|
<dl><dt>
|
|
<tt>set_curcfg( <i>id</i> INT <em>|</em> <i>ts_name</i> TEXT
|
|
) RETURNS VOID</tt>
|
|
</dt><dd>
|
|
Set the current configuration used by <tt>to_tsvector</tt>
|
|
and <tt>to_tsquery</tt>.
|
|
</dd><dt>
|
|
<tt>show_curcfg() RETURNS INT4</tt>
|
|
</dt><dd>
|
|
Returns the integer <tt>id</tt> of the current configuration.
|
|
</dd></dl>
|
|
|
|
<p>
|
|
Each configuration is defined by a record in the <tt>pg_ts_cfg</tt> table:
|
|
|
|
</p><pre>create table pg_ts_cfg (
|
|
id int not null primary key,
|
|
ts_name text not null,
|
|
prs_name text not null,
|
|
locale text
|
|
);</pre>
|
|
|
|
The <tt>id</tt> and <tt>ts_name</tt> are unique values
|
|
which identify the configuration;
|
|
the <tt>prs_name</tt> specifies which parser the configuration uses.
|
|
Once this parser has split document text into tokens,
|
|
the type of each resulting token --
|
|
or, more specifically, the type's <tt>tok_alias</tt>
|
|
as specified in the parser's <tt>lexem_type()</tt> table --
|
|
is searched for together with the configuration's <tt>ts_name</tt>
|
|
in the <tt>pg_ts_cfgmap</tt> table:
|
|
|
|
<pre>create table pg_ts_cfgmap (
|
|
ts_name text not null,
|
|
tok_alias text not null,
|
|
dict_name text[],
|
|
primary key (ts_name,tok_alias)
|
|
);</pre>
|
|
|
|
Those tokens whose types are not listed are discarded.
|
|
The remaining tokens are assigned integer positions,
|
|
starting with 1 for the first token in the document,
|
|
and turned into lexemes with the help of the dictionaries
|
|
whose names are given in the <tt>dict_name</tt> array for their type.
|
|
These dictionaries are tried in order,
|
|
stopping either with the first one to return a lexeme for the token,
|
|
or discarding the token if no dictionary returns a lexeme for it.
|
|
|
|
<h2><a name="dictionaries">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 oid not null,
|
|
prs_nexttoken oid not null,
|
|
prs_end oid not null,
|
|
prs_headline oid not null,
|
|
prs_lextype oid 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.
|
|
<br>
|
|
Example:
|
|
<br>
|
|
<pre> apod=# select m.ts_name, t.alias as tok_type, t.descr as description, p.token,\
|
|
apod=# m.dict_name, strip(to_tsvector(p.token)) as tsvector\
|
|
apod=# from parse('Tsearch module for PostgreSQL 7.3.3') as\
|
|
apod=# p, token_type() as t, pg_ts_cfgmap as m, pg_ts_cfg as c\
|
|
apod=# where t.tokid=p.tokid and t.alias = m.tok_alias\
|
|
apod=# and m.ts_name=c.ts_name and c.oid=show_curcfg();
|
|
ts_name | tok_type | description | token | dict_name | tsvector
|
|
---------+----------+-------------+------------+-----------+--------------
|
|
default | lword | Latin word | Tsearch | {en_stem} | 'tsearch'
|
|
default | word | Word | module | {simple} | '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:
|
|
<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 will be used for the token
|
|
</li><li> tsvector - final result
|
|
</li></ul>
|
|
|
|
</dd><dt>
|
|
<tt>CREATE FUNCTION parse(
|
|
<em>[</em> <i>parser</i>, <em>]</em> <i>document</i> TEXT
|
|
) RETURNS SETOF tokenout</tt>
|
|
</dt><dd>
|
|
Parses the given document and returns a series of records,
|
|
one for each token produced by parsing.
|
|
Each token includes a <tt>tokid</tt> giving its type
|
|
and a <tt>lexem</tt> which gives its content.
|
|
</dd></dl>
|
|
|
|
<h2><a name="dictionaries">Dictionaries</a></h2>
|
|
|
|
Dictionaries take textual tokens as input,
|
|
usually those produced by a parser,
|
|
and return lexemes which are usually some reduced form of the token.
|
|
Among the dictionaries which come installed with tsearch2 are:
|
|
|
|
<ul>
|
|
<li><b>simple</b> simply folds uppercase letters to lowercase
|
|
before returning the word.
|
|
</li><li><b>en_stem</b> runs an English Snowball stemmer on each word
|
|
that attempts to reduce the various forms of a verb or noun
|
|
to a single recognizable form.
|
|
</li><li><b>ru_stem</b> runs a Russian Snowball stemmer on each word.
|
|
</li></ul>
|
|
|
|
Each dictionary is defined by an entry in the <tt>pg_ts_dict</tt> table:
|
|
|
|
<pre>CREATE TABLE pg_ts_dict (
|
|
dict_name text not null,
|
|
dict_init oid,
|
|
dict_initoption text,
|
|
dict_lexize oid not null,
|
|
dict_comment text
|
|
);</pre>
|
|
|
|
The <tt>dict_name</tt>
|
|
serve as unique identifiers for the dictionary.
|
|
The meaning of the <tt>dict_initoption</tt> varies among dictionaries,
|
|
but for the built-in Snowball dictionaries
|
|
it specifies a file from which stop words should be read.
|
|
The <tt>dict_comment</tt> is a human-readable description of the dictionary.
|
|
The other fields are internal function identifiers
|
|
useful only to developers trying to implement their own dictionaries.
|
|
<p>
|
|
The argument named <tt><i>dictionary</i></tt>
|
|
in each of the following functions
|
|
should be <tt>dict_name</tt>
|
|
identifying which dictionary should be used for the operation;
|
|
if omitted then the current dictionary is used.
|
|
|
|
</p><dl><dt>
|
|
<tt>CREATE FUNCTION set_curdict(<i>dictionary</i>) RETURNS VOID</tt>
|
|
</dt><dd>
|
|
Selects a current dictionary for use by functions
|
|
that do not select a dictionary explicitly.
|
|
</dd><dt>
|
|
<tt>CREATE FUNCTION lexize(
|
|
<em>[</em> <i>dictionary</i>, <em>]</em> <i>word</i> text)
|
|
RETURNS TEXT[]</tt>
|
|
</dt><dd>
|
|
Reduces a single word to a lexeme.
|
|
Note that lexemes are arrays of zero or more strings,
|
|
since in some languages there might be several base words
|
|
from which an inflected form could arise.
|
|
</dd></dl>
|
|
|
|
<h2><a name="ranking">Ranking</a></h2>
|
|
|
|
Ranking attempts to measure how relevant documents are to particular queries
|
|
by inspecting the number of times each search word appears in the document,
|
|
and whether different search terms occur near each other.
|
|
Note that this information is only available in unstripped vectors --
|
|
ranking functions will only return a useful result
|
|
for a <tt>tsvector</tt> which still has position information!
|
|
<p>
|
|
Both of these ranking functions
|
|
take an integer <i>normalization</i> option
|
|
that specifies whether a document's length should impact its rank.
|
|
This is often desirable,
|
|
since a hundred-word document with five instances of a search word
|
|
is probably more relevant than a thousand-word document with five instances.
|
|
The option can have the values:
|
|
|
|
</p><ul>
|
|
<li><tt>0</tt> (the default) ignores document length.
|
|
</li><li><tt>1</tt> divides the rank by the logarithm of the length.
|
|
</li><li><tt>2</tt> divides the rank by the length itself.
|
|
</li></ul>
|
|
|
|
The two ranking functions currently available are:
|
|
|
|
<dl><dt>
|
|
<tt>CREATE FUNCTION rank(<br>
|
|
<em>[</em> <i>weights</i> float4[], <em>]</em>
|
|
<i>vector</i> tsvector, <i>query</i> tsquery,
|
|
<em>[</em> <i>normalization</i> int4 <em>]</em><br>
|
|
) RETURNS float4</tt>
|
|
</dt><dd>
|
|
This is the ranking function from the old version of OpenFTS,
|
|
and offers the ability to weight word instances more heavily
|
|
depending on how you have classified them.
|
|
The <i>weights</i> specify how heavily to weight each category of word:
|
|
<pre>{<i>D-weight</i>, <i>C-weight</i>, <i>B-weight</i>, <i>A-weight</i>}</pre>
|
|
If no weights are provided, then these defaults are used:
|
|
<pre>{0.1, 0.2, 0.4, 1.0}</pre>
|
|
Often weights are used to mark words from special areas of the document,
|
|
like the title or an initial abstract,
|
|
and make them more or less important than words in the document body.
|
|
</dd><dt>
|
|
<tt>CREATE FUNCTION rank_cd(<br>
|
|
<em>[</em> <i>K</i> int4, <em>]</em>
|
|
<i>vector</i> tsvector, <i>query</i> tsquery,
|
|
<em>[</em> <i>normalization</i> int4 <em>]</em><br>
|
|
) RETURNS float4</tt>
|
|
</dt><dd>
|
|
This function computes the cover density ranking
|
|
for the given document <i>vector</i> and <i>query</i>,
|
|
as described in Clarke, Cormack, and Tudhope's
|
|
"<a href="http://citeseer.nj.nec.com/clarke00relevance.html">Relevance Ranking for One to Three Term Queries</a>"
|
|
in the 1999 <i>Information Processing and Management</i>.
|
|
The value <i>K</i> is one of the values from their formula,
|
|
and defaults to <i>K</i>=4.
|
|
The examples in their paper <i>K</i>=16;
|
|
we can roughly describe the term
|
|
as stating how far apart two search terms can fall
|
|
before the formula begins penalizing them for lack of proximity.
|
|
</dd></dl>
|
|
|
|
<h2><a name="headlines">Headlines</a></h2>
|
|
|
|
<dl><dt>
|
|
<tt>CREATE FUNCTION headline(<br>
|
|
<em>[</em> <i>id</i> int4, <em>|</em> <i>ts_name</i> text, <em>]</em>
|
|
<i>document</i> text, <i>query</i> tsquery,
|
|
<em>[</em> <i>options</i> text <em>]</em><br>
|
|
) RETURNS text</tt>
|
|
</dt><dd>
|
|
Every form of the the <tt>headline()</tt> function
|
|
accepts a <tt>document</tt> along with a <tt>query</tt>,
|
|
and returns one or more ellipse-separated excerpts from the document
|
|
in which terms from the query are highlighted.
|
|
The configuration with which to parse the document
|
|
can be specified by either its <i>id</i> or <i>ts_name</i>;
|
|
if none is specified that the current configuration is used instead.
|
|
<p>
|
|
An <i>options</i> string if provided should be a comma-separated list
|
|
of one or more '<i>option</i><tt>=</tt><i>value</i>' pairs.
|
|
The available options are:
|
|
</p><ul>
|
|
<li><tt>StartSel</tt>, <tt>StopSel</tt> --
|
|
the strings with which query words appearing in the document
|
|
should be delimited to distinguish them from other excerpted words.
|
|
</li><li><tt>MaxWords</tt>, <tt>MinWords</tt> --
|
|
limits on the shortest and longest headlines you will accept.
|
|
</li><li><tt>ShortWord</tt> --
|
|
this prevents your headline from beginning or ending
|
|
with a word which has this many characters or less.
|
|
The default value of <tt>3</tt> should eliminate most English
|
|
conjunctions and articles.
|
|
</li></ul>
|
|
Any unspecified options receive these defaults:
|
|
<pre>StartSel=<b>, StopSel=</b>, MaxWords=35, MinWords=15, ShortWord=3
|
|
</pre>
|
|
</dd></dl>
|
|
|
|
</body></html> |