141 lines
4.0 KiB
Plaintext
141 lines
4.0 KiB
Plaintext
[[index-modules-similarity]]
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== Similarity module
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A similarity (scoring / ranking model) defines how matching documents
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are scored. Similarity is per field, meaning that via the mapping one
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can define a different similarity per field.
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Configuring a custom similarity is considered a expert feature and the
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builtin similarities are most likely sufficient as is described in the
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<<mapping-core-types,mapping section>>
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[float]
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[[configuration]]
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=== Configuring a similarity
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Most existing or custom Similarities have configuration options which
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can be configured via the index settings as shown below. The index
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options can be provided when creating an index or updating index
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settings.
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[source,js]
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--------------------------------------------------
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"similarity" : {
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"my_similarity" : {
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"type" : "DFR",
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"basic_model" : "g",
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"after_effect" : "l",
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"normalization" : "h2",
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"normalization.h2.c" : "3.0"
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}
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}
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--------------------------------------------------
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Here we configure the DFRSimilarity so it can be referenced as
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`my_similarity` in mappings as is illustrate in the below example:
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[source,js]
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--------------------------------------------------
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{
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"book" : {
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"properties" : {
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"title" : { "type" : "string", "similarity" : "my_similarity" }
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}
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}
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--------------------------------------------------
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[float]
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=== Available similarities
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[float]
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[[default-similarity]]
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==== Default similarity
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The default similarity that is based on the TF/IDF model. This
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similarity has the following option:
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`discount_overlaps`::
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Determines whether overlap tokens (Tokens with
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0 position increment) are ignored when computing norm. By default this
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is true, meaning overlap tokens do not count when computing norms.
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Type name: `default`
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[float]
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[[bm25]]
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==== BM25 similarity
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Another TF/IDF based similarity that has built-in tf normalization and
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is supposed to work better for short fields (like names). See
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http://en.wikipedia.org/wiki/Okapi_BM25[Okapi_BM25] for more details.
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This similarity has the following options:
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[horizontal]
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`k1`::
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Controls non-linear term frequency normalization
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(saturation).
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`b`::
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Controls to what degree document length normalizes tf values.
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`discount_overlaps`::
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Determines whether overlap tokens (Tokens with
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0 position increment) are ignored when computing norm. By default this
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is true, meaning overlap tokens do not count when computing norms.
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Type name: `BM25`
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[float]
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[[drf]]
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==== DRF similarity
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Similarity that implements the
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http://lucene.apache.org/core/4_1_0/core/org/apache/lucene/search/similarities/DFRSimilarity.html[divergence
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from randomness] framework. This similarity has the following options:
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[horizontal]
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`basic_model`::
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Possible values: `be`, `d`, `g`, `if`, `in`, `ine` and `p`.
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`after_effect`::
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Possible values: `no`, `b` and `l`.
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`normalization`::
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Possible values: `no`, `h1`, `h2`, `h3` and `z`.
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All options but the first option need a normalization value.
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Type name: `DFR`
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[float]
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[[ib]]
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==== IB similarity.
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http://lucene.apache.org/core/4_1_0/core/org/apache/lucene/search/similarities/IBSimilarity.html[Information
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based model] . This similarity has the following options:
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[horizontal]
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`distribution`:: Possible values: `ll` and `spl`.
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`lambda`:: Possible values: `df` and `ttf`.
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`normalization`:: Same as in `DFR` similarity.
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Type name: `IB`
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[float]
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[[default-base]]
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==== Default and Base Similarities
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By default, Elasticsearch will use whatever similarity is configured as
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`default`. However, the similarity functions `queryNorm()` and `coord()`
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are not per-field. Consequently, for expert users wanting to change the
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implementation used for these two methods, while not changing the
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`default`, it is possible to configure a similarity with the name
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`base`. This similarity will then be used for the two methods.
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You can change the default similarity for all fields like this:
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[source,js]
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--------------------------------------------------
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index.similarity.default.type: BM25
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--------------------------------------------------
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