OpenSearch/docs/reference/search/suggesters/term-suggest.asciidoc

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[[search-suggesters-term]]
=== Term suggester
NOTE: In order to understand the format of suggestions, please
read the <<search-suggesters>> page first.
The `term` suggester suggests terms based on edit distance. The provided
suggest text is analyzed before terms are suggested. The suggested terms
are provided per analyzed suggest text token. The `term` suggester
doesn't take the query into account that is part of request.
==== Common suggest options:
[horizontal]
`text`::
The suggest text. The suggest text is a required option that
needs to be set globally or per suggestion.
`field`::
The field to fetch the candidate suggestions from. This is
an required option that either needs to be set globally or per
suggestion.
`analyzer`::
The analyzer to analyse the suggest text with. Defaults
to the search analyzer of the suggest field.
`size`::
The maximum corrections to be returned per suggest text
token.
`sort`::
Defines how suggestions should be sorted per suggest text
term. Two possible values:
+
** `score`: Sort by score first, then document frequency and
then the term itself.
** `frequency`: Sort by document frequency first, then similarity
score and then the term itself.
+
`suggest_mode`::
The suggest mode controls what suggestions are
included or controls for what suggest text terms, suggestions should be
suggested. Three possible values can be specified:
+
** `missing`: Only provide suggestions for suggest text terms that are
not in the index. This is the default.
** `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.
==== Other term suggest options:
[horizontal]
`lowercase_terms`::
Lower cases the suggest text terms after text analysis.
`max_edits`::
The maximum edit distance candidate suggestions can
have in order to be considered as a suggestion. Can only be a value
between 1 and 2. Any other value result in an bad request error being
thrown. Defaults to 2.
`prefix_length`::
The number of minimal prefix characters that must
match in order be a candidate suggestions. Defaults to 1. Increasing
this number improves spellcheck performance. Usually misspellings don't
occur in the beginning of terms. (Old name "prefix_len" is deprecated)
`min_word_length`::
The minimum length a suggest text term must have in
order to be included. Defaults to 4. (Old name "min_word_len" is deprecated)
`shard_size`::
Sets the maximum number of suggestions to be retrieved
from each individual shard. During the reduce phase only the top N
suggestions are returned based on the `size` option. Defaults to the
`size` option. Setting this to a value higher than the `size` can be
useful in order to get a more accurate document frequency for spelling
corrections at the cost of performance. Due to the fact that terms are
partitioned amongst shards, the shard level document frequencies of
spelling corrections may not be precise. Increasing this will make these
document frequencies more precise.
`max_inspections`::
A factor that is used to multiply with the
`shards_size` in order to inspect more candidate spell corrections on
the shard level. Can improve accuracy at the cost of performance.
Defaults to 5.
`min_doc_freq`::
The minimal threshold in number of documents a
suggestion should appear in. This can be specified as an absolute number
or as a relative percentage of number of documents. This can improve
quality by only suggesting high frequency terms. Defaults to 0f and is
not enabled. If a value higher than 1 is specified then the number
cannot be fractional. The shard level document frequencies are used for
this option.
`max_term_freq`::
The maximum threshold in number of documents a
suggest text token can exist in order to be included. Can be a relative
percentage number (e.g 0.4) or an absolute number to represent document
frequencies. If an value higher than 1 is specified then fractional can
not be specified. Defaults to 0.01f. This can be used to exclude high
frequency terms from being spellchecked. High frequency terms are
usually spelled correctly on top of this also improves the spellcheck
performance. The shard level document frequencies are used for this
option.
`string_distance`::
Which string distance implementation to use for comparing how similar
suggested terms are. Five possible values can be specfied:
`internal` - The default based on damerau_levenshtein but highly optimized
for comparing string distancee 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.