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
) returnstsvector
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
,
plainto_tsquery
, and
phraseto_tsquery
for converting a query to the tsquery
data type.
to_tsquery
offers access to more features
than either plainto_tsquery
or
phraseto_tsquery
, but it is less forgiving
about its input.
to_tsquery([config
regconfig
, ]querytext
text
) returnstsquery
to_tsquery
creates a tsquery
value from
querytext
, which must consist of single tokens
separated by the tsquery
operators &
(AND),
|
(OR), !
(NOT), and
<->
(FOLLOWED BY), possibly grouped
using parentheses. In other words, the input to
to_tsquery
must already follow the general rules for
tsquery
input, as described in Section 8.11.2. The difference is that while basic
tsquery
input takes the tokens at face value,
to_tsquery
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:
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 FOLLOWED BY
operator.
plainto_tsquery([config
regconfig
, ]querytext
text
) returnstsquery
plainto_tsquery
transforms the unformatted text
querytext
to a tsquery
value.
The text is parsed and normalized much as for to_tsvector
,
then the &
(AND) tsquery
operator is
inserted between surviving words.
Example:
SELECT plainto_tsquery('english', 'The Fat Rats'); plainto_tsquery ----------------- 'fat' & 'rat'
Note that plainto_tsquery
will not
recognize tsquery
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.
phraseto_tsquery([config
regconfig
, ]querytext
text
) returnstsquery
phraseto_tsquery
behaves much like
plainto_tsquery
, except that it inserts
the <->
(FOLLOWED BY) operator between
surviving words instead of the &
(AND) operator.
Also, stop words are not simply discarded, but are accounted for by
inserting <
operators rather
than N
><->
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.
Example:
SELECT phraseto_tsquery('english', 'The Fat Rats'); phraseto_tsquery ------------------ 'fat' <-> 'rat'
Like plainto_tsquery
, the
phraseto_tsquery
function will not
recognize tsquery
operators, weight labels,
or prefix-match labels in its input:
SELECT phraseto_tsquery('english', 'The Fat & Rats:C'); phraseto_tsquery ----------------------------- 'fat' <-> 'rat' <-> 'c'
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
Ranks vectors based on the frequency of their matching lexemes.
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. Cover density is similar to ts_rank
ranking
except that the proximity of matching lexemes to each other is
taken into consideration.
This function requires lexeme positional information to perform
its calculation. Therefore, it ignores any “stripped”
lexemes in the tsvector
. If there are no unstripped
lexemes in the input, the result will be zero. (See Section 12.4.1 for more information
about the strip
function and positional information
in tsvector
s.)
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
]) returnstext
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.