©
This document uses PHP Chinese website manual Release
To implement full text searching there must be a function to create a tsvector from a document and a tsquery 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. PostgreSQL provides support for all of these functions.
PostgreSQL provides the
function to_tsvector for converting a document to
the tsvector data type.
to_tsvector([ config regconfig, ] document text) returns tsvector
to_tsvector parses a textual document into tokens,
reduces the tokens to lexemes, and returns a tsvector 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:
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
In the example above we see that the resulting tsvector does not contain the words a, on, or it, the word rats became rat, and the punctuation sign - was ignored.
The to_tsvector 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 (Section 12.6) is consulted,
where the list can vary depending on the token type. The first dictionary
that recognizes the token emits one or more normalized
lexemes to represent the token. For example,
rats became rat because one of the
dictionaries recognized that the word rats is a plural
form of rat. Some words are recognized as
stop words (Section 12.6.1), which
causes them to be ignored since they occur too frequently to be useful in
searching. In our example these are
a, on, and it.
If no dictionary in the list recognizes the token then it is also ignored.
In this example that happened to the punctuation sign -
because there are in fact no dictionaries assigned for its token type
(Space symbols), 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 (Section 12.7). 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 english for the
English language.
The function setweight can be used to label the
entries of a tsvector with a given weight,
where a weight is one of the letters A, B,
C, or D.
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.
Because to_tsvector(NULL) will
return NULL, it is recommended to use
coalesce whenever a field might be null.
Here is the recommended method for creating
a tsvector from a structured document:
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');
Here we have used setweight to label the source
of each lexeme in the finished tsvector, and then merged
the labeled tsvector values using the tsvector
concatenation operator ||. (Section 12.4.1 gives details about these
operations.)
PostgreSQL provides the
functions to_tsquery and
plainto_tsquery for converting a query to
the tsquery data type. to_tsquery
offers access to more features than plainto_tsquery,
but is less forgiving about its input.
to_tsquery([ config regconfig, ] querytext text) returns tsquery
to_tsquery creates a tsquery value from
querytext, which must consist of single tokens
separated by the Boolean operators & (AND),
| (OR) and ! (NOT). These operators
can be grouped using parentheses. In other words, the input to
to_tsquery must already follow the general rules for
tsquery input, as described in Section 8.11. The difference is that while basic
tsquery input takes the tokens at face value,
to_tsquery 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:
SELECT to_tsquery('english', 'The & Fat & Rats');
to_tsquery
---------------
'fat' & 'rat'
As in basic tsquery input, weight(s) can be attached to each lexeme to restrict it to match only tsvector lexemes of those weight(s). For example:
SELECT to_tsquery('english', 'Fat | Rats:AB');
to_tsquery
------------------
'fat' | 'rat':AB
Also, * can be attached to a lexeme to specify prefix matching:
SELECT to_tsquery('supern:*A & star:A*B');
to_tsquery
--------------------------
'supern':*A & 'star':*AB
Such a lexeme will match any word in a tsvector that begins with the given string.
to_tsquery 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 supernovae
stars : sn:
SELECT to_tsquery('''supernovae stars'' & !crab');
to_tsquery
---------------
'sn' & !'crab'
Without quotes, to_tsquery will generate a syntax
error for tokens that are not separated by an AND or OR operator.
plainto_tsquery([ config regconfig, ] querytext text) returns tsquery
plainto_tsquery transforms unformatted text
querytext to tsquery.
The text is parsed and normalized much as for to_tsvector,
then the & (AND) Boolean operator is inserted
between surviving words.
Example:
SELECT plainto_tsquery('english', 'The Fat Rats');
plainto_tsquery
-----------------
'fat' & 'rat'
Note that plainto_tsquery cannot
recognize Boolean operators, weight labels, or prefix-match labels
in its input:
SELECT plainto_tsquery('english', 'The Fat & Rats:C');
plainto_tsquery
---------------------
'fat' & 'rat' & 'c'
Here, all the input punctuation was discarded as being space symbols.
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. PostgreSQL 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.
The two ranking functions currently available are:
ts_rank([ weights float4[], ] vector tsvector, query tsquery [, normalization integer ]) returns float4
Standard ranking function.
ts_rank_cd([ weights float4[], ] vector tsvector, query tsquery [, normalization integer ]) returns float4
This function computes the cover density 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.
This function requires positional information in its input. Therefore it will not work on "stripped" tsvector values — it will always return zero.
For both these functions, the optional weights 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:
{D-weight, C-weight, B-weight, A-weight}
If no weights are provided, then these defaults are used:
{0.1, 0.2, 0.4, 1.0}
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.
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 normalization 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 | (for example, 2|4).
0 (the default) ignores the document length
1 divides the rank by 1 + the logarithm of the document length
2 divides the rank by the document length
4 divides the rank by the mean harmonic distance between extents
(this is implemented only by ts_rank_cd)
8 divides the rank by the number of unique words in document
16 divides the rank by 1 + the logarithm of the number of unique words in document
32 divides the rank by itself + 1
If more than one flag bit is specified, the transformations are applied in the order listed.
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 (rank/(rank+1)) 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.
Here is an example that selects only the ten highest-ranked matches:
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
This is the same example using normalized ranking:
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
Ranking can be expensive since it requires consulting the tsvector 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.
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. PostgreSQL
provides a function ts_headline that
implements this functionality.
ts_headline([ config regconfig, ] document text, query tsquery [, options text ]) returns text
ts_headline 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
config; if config
is omitted, the
default_text_search_config configuration is used.
If an options string is specified it must consist of a comma-separated list of one or more option=value pairs. The available options are:
StartSel, StopSel: 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.
MaxWords, MinWords: these numbers determine the longest and shortest headlines to output.
ShortWord: 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.
HighlightAll: Boolean flag; if true the whole document will be used as the headline, ignoring the preceding three parameters.
MaxFragments: 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 MaxWords and words of length ShortWord 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 MinWords in the document will be displayed.
FragmentDelimiter: When more than one fragment is displayed, the fragments will be separated by this string.
Any unspecified options receive these defaults:
StartSel=<b>, StopSel=</b>, MaxWords=35, MinWords=15, ShortWord=3, HighlightAll=FALSE, MaxFragments=0, FragmentDelimiter=" ... "
For example:
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>.
ts_headline uses the original document, not a
tsvector summary, so it can be slow and should be used with
care. A typical mistake is to call ts_headline for
every matching document when only ten documents are
to be shown. SQL subqueries can help; here is an
example:
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;