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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"> <HTML ><HEAD ><TITLE >Controlling Text Search</TITLE ><META NAME="GENERATOR" CONTENT="Modular DocBook HTML Stylesheet Version 1.79"><LINK REV="MADE" HREF="mailto:pgsql-docs@postgresql.org"><LINK REL="HOME" TITLE="PostgreSQL 9.2.24 Documentation" HREF="index.html"><LINK REL="UP" TITLE="Full Text Search" HREF="textsearch.html"><LINK REL="PREVIOUS" TITLE="Tables and Indexes" HREF="textsearch-tables.html"><LINK REL="NEXT" TITLE="Additional Features" HREF="textsearch-features.html"><LINK REL="STYLESHEET" TYPE="text/css" HREF="stylesheet.css"><META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=ISO-8859-1"><META NAME="creation" CONTENT="2017-11-06T22:43:11"></HEAD ><BODY CLASS="SECT1" ><DIV CLASS="NAVHEADER" ><TABLE SUMMARY="Header navigation table" WIDTH="100%" BORDER="0" CELLPADDING="0" CELLSPACING="0" ><TR ><TH COLSPAN="5" ALIGN="center" VALIGN="bottom" ><A HREF="index.html" >PostgreSQL 9.2.24 Documentation</A ></TH ></TR ><TR ><TD WIDTH="10%" ALIGN="left" VALIGN="top" ><A TITLE="Tables and Indexes" HREF="textsearch-tables.html" ACCESSKEY="P" >Prev</A ></TD ><TD WIDTH="10%" ALIGN="left" VALIGN="top" ><A HREF="textsearch.html" ACCESSKEY="U" >Up</A ></TD ><TD WIDTH="60%" ALIGN="center" VALIGN="bottom" >Chapter 12. Full Text Search</TD ><TD WIDTH="20%" ALIGN="right" VALIGN="top" ><A TITLE="Additional Features" HREF="textsearch-features.html" ACCESSKEY="N" >Next</A ></TD ></TR ></TABLE ><HR ALIGN="LEFT" WIDTH="100%"></DIV ><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 ><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="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 <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 ><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="SCREEN" >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="SCREEN" >SELECT to_tsquery('english', 'Fat | Rats:AB'); to_tsquery ------------------ 'fat' | 'rat':AB</PRE ><P> Also, <TT CLASS="LITERAL" >*</TT > 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 <TT CLASS="TYPE" >tsvector</TT > 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 <TT CLASS="LITERAL" >supernovae stars : sn</TT >: </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 operator. </P ><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="SCREEN" >SELECT plainto_tsquery('english', 'The Fat Rats'); plainto_tsquery ----------------- 'fat' & 'rat'</PRE ><P> Note that <CODE CLASS="FUNCTION" >plainto_tsquery</CODE > cannot recognize Boolean 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 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 > Ranks vectors based on the frequency of their matching lexemes. </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="SYNOPSIS" >{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 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 <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="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 <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 ><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 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. </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 common 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 used as the headline, ignoring the preceding three parameters. </P ></LI ><LI STYLE="list-style-type: disc" ><P > <TT CLASS="LITERAL" >MaxFragments</TT >: 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 <TT CLASS="LITERAL" >MaxWords</TT > and words of length <TT CLASS="LITERAL" >ShortWord</TT > 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 <TT CLASS="LITERAL" >MinWords</TT > in the document will be displayed. </P ></LI ><LI STYLE="list-style-type: disc" ><P > <TT CLASS="LITERAL" >FragmentDelimiter</TT >: When more than one fragment is displayed, the fragments will be separated by this string. </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, MaxFragments=0, FragmentDelimiter=" ... "</PRE ><P> </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('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>.</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> </P ></DIV ></DIV ><DIV CLASS="NAVFOOTER" ><HR ALIGN="LEFT" WIDTH="100%"><TABLE SUMMARY="Footer navigation table" WIDTH="100%" BORDER="0" CELLPADDING="0" CELLSPACING="0" ><TR ><TD WIDTH="33%" ALIGN="left" VALIGN="top" ><A HREF="textsearch-tables.html" ACCESSKEY="P" >Prev</A ></TD ><TD WIDTH="34%" ALIGN="center" VALIGN="top" ><A HREF="index.html" ACCESSKEY="H" >Home</A ></TD ><TD WIDTH="33%" ALIGN="right" VALIGN="top" ><A HREF="textsearch-features.html" ACCESSKEY="N" >Next</A ></TD ></TR ><TR ><TD WIDTH="33%" ALIGN="left" VALIGN="top" >Tables and Indexes</TD ><TD WIDTH="34%" ALIGN="center" VALIGN="top" ><A HREF="textsearch.html" ACCESSKEY="U" >Up</A ></TD ><TD WIDTH="33%" ALIGN="right" VALIGN="top" >Additional Features</TD ></TR ></TABLE ></DIV ></BODY ></HTML >