PostgreSQL

12.3.1. Parsing Documents

12.3.2. Parsing Queries

12.3.3. Ranking Search Results

12.3.4. Highlighting Results

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 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:

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:

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, phraseto_tsquery and websearch_to_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. websearch_to_tsquery is a simplified version of to_tsquery with an alternative syntax, similar to the one used by web search engines.

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:

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:

Also, * can be attached to a lexeme to specify prefix matching:

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:

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 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:

Note that plainto_tsquery will not recognize tsquery operators, weight labels, or prefix-match labels in its input:

Here, all the input punctuation was discarded.

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 <`N>` operators rather than <-> 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:

Like plainto_tsquery, the phraseto_tsquery function will not recognize tsquery operators, weight labels, or prefix-match labels in its input:

websearch_to_tsquery creates a tsquery value from `querytext using an alternative syntax in which simple unformatted text is a valid query. Unlike `plainto_tsquery and phraseto_tsquery, 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:

Other punctuation is ignored. So like plainto_tsquery and phraseto_tsquery, the websearch_to_tsquery function will not recognize tsquery operators, weight labels, or prefix-match labels in its input.

Examples:

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:

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:

If no `weights` are provided, then these defaults are used:

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).

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:

This is the same example using normalized ranking:

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 accepts a document along with a query, and returns an excerpt from the document in which terms from the query are highlighted. Specifically, the function will use the query to select relevant text fragments, and then highlight all words that appear in the query, even if those word positions do not match the query’s restrictions. 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:

These option names are recognized case-insensitively. You must double-quote string values if they contain spaces or commas.

In non-fragment-based headline generation, ts_headline locates matches for the given `query and chooses a single one to display, preferring matches that have more query words within the allowed headline length. In fragment-based headline generation, `ts_headline locates the query matches and splits each match into “[.quote]#fragments”# of no more than MaxWords words each, preferring fragments with more query words, and when possible “[.quote]#stretching”# 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 MinWords words in the document will be displayed.

For example:

ts_headline uses the original document, not a tsvector summary, so it can be slow and should be used with care.

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