elastic.js: Module: TermSuggester

Module

TermSuggester


        TermSuggester ( name )

A suggester that suggests terms based on edit distance.

Since: elasticsearch 0.90

Parameters: name <String> The name which be used to refer to this suggester.

Method Summary

Returns	Name	Description
`String`	`_type()`	The type of ejs object. For internal use only.
`Object`	`accuracy(a)`	Sets the accuracy. How similar the suggested terms at least need to be compared to the original suggest text.
`Object`	`analyzer(analyzer)`	Sets analyzer used to analyze the suggest text.
`Object`	`field(field)`	Sets the field used to generate suggestions from.
`Object`	`maxEdits(max)`	Sets the maximum edit distance candidate suggestions can have in order to be considered as a suggestion.
`Object`	`maxInspections(max)`	The factor that is used to multiply with the size in order to inspect more candidate suggestions.
`Object`	`maxTermFreq(max)`	Sets a maximum threshold in number of documents a suggest text token can exist in order to be corrected.
`Object`	`minDocFreq(min)`	Sets a minimal threshold of the number of documents a suggested term should appear in.
`Object`	`minWordLen(len)`	Sets the minimum length a suggest text term must have in order to be corrected.
`Object`	`shardSize(s)`	Sets the maximum number of suggestions to be retrieved from each individual shard.
`Object`	`size(s)`	Sets the number of suggestions returned for each token.
`Object`	`sort(s)`	Sets the sort mode. Valid values are: `score` - Sort by score first, then document frequency, and then the term itself `frequency` - Sort by document frequency first, then simlarity score and then the term itself
`Object`	`stringDistance(s)`	Sets what string distance implementation to use for comparing how similar suggested terms are. Valid values are: `internal` - based on damerau_levenshtein but but highly optimized for comparing string distance for terms inside the index `damerau_levenshtein` - String distance algorithm based on Damerau-Levenshtein algorithm `levenstein` - String distance algorithm based on Levenstein edit distance algorithm `jarowinkler` - String distance algorithm based on Jaro-Winkler algorithm `ngram` - String distance algorithm based on character n-grams
`Object`	`suggestMode(m)`	Sets the suggest mode. Valid values are: `missing` - Only suggest terms in the suggest text that aren't in the index `popular` - Only suggest suggestions that occur in more docs then the original suggest text term `always` - Suggest any matching suggestions based on terms in the suggest text
`Object`	`text(txt)`	Sets the text to get suggestions for. If not set, the global suggestion text will be used.
`String`	`toJSON()`	Retrieves the internal `suggest` object. This is typically used by internal API functions so use with caution.

Method Detail

_type


        String _type ( )

The type of ejs object. For internal use only.

Returns: the type of object

accuracy


        Object accuracy ( a )

Sets the accuracy. How similar the suggested terms at least need to be compared to the original suggest text.

Parameters: a <Double> A positive double value between 0 and 1.
Returns: returns this so that calls can be chained.

analyzer


        Object analyzer ( analyzer )

Sets analyzer used to analyze the suggest text.

Parameters: analyzer <String> A valid analyzer name.
Returns: returns this so that calls can be chained.

field


        Object field ( field )

Sets the field used to generate suggestions from.

Parameters: field <String> A valid field name.
Returns: returns this so that calls can be chained.

maxEdits


        Object maxEdits ( max )

Sets the maximum edit distance candidate suggestions can have in order to be considered as a suggestion.

Parameters: max <Integer> An integer value greater than 0.
Returns: returns this so that calls can be chained.

maxInspections


        Object maxInspections ( max )

The factor that is used to multiply with the size in order to inspect more candidate suggestions.

Parameters: max <Integer> A positive integer value.
Returns: returns this so that calls can be chained.

maxTermFreq


        Object maxTermFreq ( max )

Sets a maximum threshold in number of documents a suggest text token can exist in order to be corrected.

Parameters: max <Double> A positive double value.
Returns: returns this so that calls can be chained.

minDocFreq


        Object minDocFreq ( min )

Sets a minimal threshold of the number of documents a suggested term should appear in.

Parameters: min <Double> A positive double value.
Returns: returns this so that calls can be chained.

minWordLen


        Object minWordLen ( len )

Sets the minimum length a suggest text term must have in order to be corrected.

Parameters: len <Integer> A positive integer value.
Returns: returns this so that calls can be chained.

shardSize


        Object shardSize ( s )

Sets the maximum number of suggestions to be retrieved from each individual shard.

Parameters: s <Integer> A positive integer value.
Returns: returns this so that calls can be chained.

size


        Object size ( s )

Sets the number of suggestions returned for each token.

Parameters: s <Integer> A positive integer value.
Returns: returns this so that calls can be chained.

sort


        Object sort ( s )

Sets the sort mode. Valid values are:

score - Sort by score first, then document frequency, and then the term itself

frequency - Sort by document frequency first, then simlarity score and then the term itself

Parameters: s <String> The score type of score or frequency.
Returns: returns this so that calls can be chained.

stringDistance


        Object stringDistance ( s )

Sets what string distance implementation to use for comparing how similar suggested terms are. Valid values are:

internal - based on damerau_levenshtein but but highly optimized for comparing string distance for terms inside the index

damerau_levenshtein - String distance algorithm based on Damerau-Levenshtein algorithm

levenstein - String distance algorithm based on Levenstein edit distance algorithm

jarowinkler - String distance algorithm based on Jaro-Winkler algorithm

ngram - String distance algorithm based on character n-grams

Parameters: s <String> The string distance algorithm name.
Returns: returns this so that calls can be chained.

suggestMode


        Object suggestMode ( m )

Sets the suggest mode. Valid values are:

missing - Only suggest terms in the suggest text that aren't in the index

popular - Only suggest suggestions that occur in more docs then the original suggest text term

always - Suggest any matching suggestions based on terms in the suggest text

Parameters: m <String> The mode of missing, popular, or always.
Returns: returns this so that calls can be chained.

text


        Object text ( txt )

Sets the text to get suggestions for. If not set, the global suggestion text will be used.

Parameters: txt <String> A string to get suggestions for.
Returns: returns this so that calls can be chained.

toJSON


        String toJSON ( )

Retrieves the internal suggest object. This is typically used by internal API functions so use with caution.

Returns: returns this object's internal suggest property.

ejs ⟩ TermSuggester