1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
|
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!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 id="docContent" class="container-fluid col-10"><div 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 15.4 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 /></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.
</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 will 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>: the word <span class="quote">“<span class="quote">or</span>”</span> 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>: a dash will be converted to
the <code class="literal">!</code> operator.
</p></li></ul></div><p>
Other punctuation is ignored. So
like <code class="function">plainto_tsquery</code>
and <code class="function">phraseto_tsquery</code>,
the <code class="function">websearch_to_tsquery</code> function will not
recognize <code class="type">tsquery</code> operators, weight labels, or prefix-match
labels in its input.
</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">MaxWords</code>, <code class="literal">MinWords</code> (integers):
these numbers determine the longest and shortest headlines to output.
The default values are 35 and 15.
</p></li><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">ShortWord</code> (integer): words of this length or less
will be dropped at the start and end of a headline, unless they are
query terms. 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): if
<code class="literal">true</code> the whole document will be used as the
headline, ignoring the preceding three parameters. The default
is <code class="literal">false</code>.
</p></li><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">MaxFragments</code> (integer): maximum number of text
fragments to display. The default value of zero selects a
non-fragment-based headline generation method. A value greater
than zero selects fragment-based headline generation (see below).
</p></li><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">StartSel</code>, <code class="literal">StopSel</code> (strings):
the strings with which to delimit query words appearing in the
document, to distinguish them from other excerpted words. The
default values are <span class="quote">“<span class="quote"><code class="literal"><b></code></span>”</span> and
<span class="quote">“<span class="quote"><code class="literal"></b></code></span>”</span>, which can be suitable
for HTML output.
</p></li><li class="listitem" style="list-style-type: disc"><p>
<code class="literal">FragmentDelimiter</code> (string): When more than one
fragment is displayed, the fragments will be separated by this string.
The default is <span class="quote">“<span class="quote"><code class="literal"> ... </code></span>”</span>.
</p></li></ul></div><p>
These option names are recognized case-insensitively.
You must double-quote string values if they contain spaces or commas.
</p><p>
In non-fragment-based headline
generation, <code class="function">ts_headline</code> locates matches for the
given <em class="replaceable"><code>query</code></em> and chooses a
single one to display, preferring matches that have more query words
within the allowed headline length.
In fragment-based headline generation, <code class="function">ts_headline</code>
locates the query matches and splits each match
into <span class="quote">“<span class="quote">fragments</span>”</span> of no more than <code class="literal">MaxWords</code>
words each, preferring fragments with more query words, and when
possible <span class="quote">“<span class="quote">stretching</span>”</span> fragments to include surrounding
words. The fragment-based mode is thus more useful when the query
matches span large sections of the document, or when it's desirable to
display multiple matches.
In either mode, if no query matches can be identified, then a single
fragment of the first <code class="literal">MinWords</code> words in the document
will be displayed.
</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('english', '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',
'Search terms may occur
many times in a document,
requiring ranking of the search matches to decide which
occurrences to display in the result.',
to_tsquery('english', 'search & term'),
'MaxFragments=10, MaxWords=7, MinWords=3, StartSel=<<, StopSel=>>');
ts_headline
------------------------------------------------------------
<<Search>> <<terms>> may occur +
many times ... ranking of the <<search>> matches to decide
</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.
</p></div></div><div class="navfooter"><hr /><table width="100%" summary="Navigation footer"><tr><td width="40%" align="left"><a accesskey="p" href="textsearch-tables.html" title="12.2. Tables and Indexes">Prev</a> </td><td width="20%" align="center"><a accesskey="u" href="textsearch.html" title="Chapter 12. Full Text Search">Up</a></td><td width="40%" align="right"> <a accesskey="n" href="textsearch-features.html" title="12.4. Additional Features">Next</a></td></tr><tr><td width="40%" align="left" valign="top">12.2. Tables and Indexes </td><td width="20%" align="center"><a accesskey="h" href="index.html" title="PostgreSQL 15.4 Documentation">Home</a></td><td width="40%" align="right" valign="top"> 12.4. Additional Features</td></tr></table></div></body></html>
|
