66 lines
2.4 KiB
Plaintext
66 lines
2.4 KiB
Plaintext
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[[query-dsl-flt-query]]
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=== Fuzzy Like This Query
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Fuzzy like this query find documents that are "like" provided text by
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running it against one or more fields.
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[source,js]
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--------------------------------------------------
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{
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"fuzzy_like_this" : {
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"fields" : ["name.first", "name.last"],
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"like_text" : "text like this one",
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"max_query_terms" : 12
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}
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}
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--------------------------------------------------
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`fuzzy_like_this` can be shortened to `flt`.
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The `fuzzy_like_this` top level parameters include:
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[cols="<,<",options="header",]
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|=======================================================================
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|Parameter |Description
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|`fields` |A list of the fields to run the more like this query against.
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Defaults to the `_all` field.
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|`like_text` |The text to find documents like it, *required*.
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|`ignore_tf` |Should term frequency be ignored. Defaults to `false`.
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|`max_query_terms` |The maximum number of query terms that will be
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included in any generated query. Defaults to `25`.
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|`min_similarity` |The minimum similarity of the term variants. Defaults
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to `0.5`.
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|`prefix_length` |Length of required common prefix on variant terms.
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Defaults to `0`.
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|`boost` |Sets the boost value of the query. Defaults to `1.0`.
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|`analyzer` |The analyzer that will be used to analyze the text.
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Defaults to the analyzer associated with the field.
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|=======================================================================
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[float]
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==== How it Works
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Fuzzifies ALL terms provided as strings and then picks the best n
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differentiating terms. In effect this mixes the behaviour of FuzzyQuery
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and MoreLikeThis but with special consideration of fuzzy scoring
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factors. This generally produces good results for queries where users
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may provide details in a number of fields and have no knowledge of
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boolean query syntax and also want a degree of fuzzy matching and a fast
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query.
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For each source term the fuzzy variants are held in a BooleanQuery with
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no coord factor (because we are not looking for matches on multiple
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variants in any one doc). Additionally, a specialized TermQuery is used
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for variants and does not use that variant term's IDF because this would
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favor rarer terms, such as misspellings. Instead, all variants use the
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same IDF ranking (the one for the source query term) and this is
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factored into the variant's boost. If the source query term does not
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exist in the index the average IDF of the variants is used.
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