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Group vector queries into new section (#110722)
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Carlos Delgado
GitHub
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5 changed files with 51 additions and 29 deletions
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@ -72,14 +72,12 @@ include::query-dsl/match-all-query.asciidoc[]
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include::query-dsl/span-queries.asciidoc[]
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include::query-dsl/vector-queries.asciidoc[]
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include::query-dsl/special-queries.asciidoc[]
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include::query-dsl/term-level-queries.asciidoc[]
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include::query-dsl/text-expansion-query.asciidoc[]
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include::query-dsl/sparse-vector-query.asciidoc[]
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include::query-dsl/minimum-should-match.asciidoc[]
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include::query-dsl/multi-term-rewrite.asciidoc[]
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@ -1,5 +1,5 @@
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[[query-dsl-sparse-vector-query]]
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== Sparse vector query
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=== Sparse vector query
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++++
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<titleabbrev>Sparse vector</titleabbrev>
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@ -19,7 +19,7 @@ For example, a stored vector `{"feature_0": 0.12, "feature_1": 1.2, "feature_2":
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[discrete]
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[[sparse-vector-query-ex-request]]
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=== Example request using an {nlp} model
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==== Example request using an {nlp} model
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[source,console]
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----
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@ -37,7 +37,7 @@ GET _search
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// TEST[skip: Requires inference]
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[discrete]
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=== Example request using precomputed vectors
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==== Example request using precomputed vectors
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[source,console]
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----
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@ -55,7 +55,7 @@ GET _search
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[discrete]
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[[sparse-vector-field-params]]
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=== Top level parameters for `sparse_vector`
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==== Top level parameters for `sparse_vector`
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`field`::
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(Required, string) The name of the field that contains the token-weight pairs to be searched against.
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@ -120,7 +120,7 @@ NOTE: The default values for `tokens_freq_ratio_threshold` and `tokens_weight_th
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[discrete]
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[[sparse-vector-query-example]]
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=== Example ELSER query
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==== Example ELSER query
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The following is an example of the `sparse_vector` query that references the ELSER model to perform semantic search.
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For a more detailed description of how to perform semantic search by using ELSER and the `sparse_vector` query, refer to <<semantic-search-elser,this tutorial>>.
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@ -241,7 +241,7 @@ GET my-index/_search
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[discrete]
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[[sparse-vector-query-with-pruning-config-and-rescore-example]]
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=== Example ELSER query with pruning configuration and rescore
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==== Example ELSER query with pruning configuration and rescore
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The following is an extension to the above example that adds a preview:[] pruning configuration to the `sparse_vector` query.
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The pruning configuration identifies non-significant tokens to prune from the query in order to improve query performance.
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@ -17,10 +17,6 @@ or collection of documents.
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This query finds queries that are stored as documents that match with
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the specified document.
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<<query-dsl-knn-query,`knn` query>>::
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A query that finds the _k_ nearest vectors to a query
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vector, as measured by a similarity metric.
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<<query-dsl-rank-feature-query,`rank_feature` query>>::
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A query that computes scores based on the values of numeric features and is
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able to efficiently skip non-competitive hits.
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@ -32,9 +28,6 @@ This query allows a script to act as a filter. Also see the
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<<query-dsl-script-score-query,`script_score` query>>::
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A query that allows to modify the score of a sub-query with a script.
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<<query-dsl-semantic-query,`semantic` query>>::
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A query that allows you to perform semantic search.
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<<query-dsl-wrapper-query,`wrapper` query>>::
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A query that accepts other queries as json or yaml string.
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@ -50,20 +43,14 @@ include::mlt-query.asciidoc[]
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include::percolate-query.asciidoc[]
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include::knn-query.asciidoc[]
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include::rank-feature-query.asciidoc[]
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include::script-query.asciidoc[]
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include::script-score-query.asciidoc[]
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include::semantic-query.asciidoc[]
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include::wrapper-query.asciidoc[]
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include::pinned-query.asciidoc[]
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include::rule-query.asciidoc[]
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include::weighted-tokens-query.asciidoc[]
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@ -1,5 +1,5 @@
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[[query-dsl-text-expansion-query]]
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== Text expansion query
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=== Text expansion query
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++++
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<titleabbrev>Text expansion</titleabbrev>
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@ -12,7 +12,7 @@ The text expansion query uses a {nlp} model to convert the query text into a lis
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[discrete]
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[[text-expansion-query-ex-request]]
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=== Example request
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==== Example request
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[source,console]
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----
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@ -32,14 +32,14 @@ GET _search
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[discrete]
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[[text-expansion-query-params]]
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=== Top level parameters for `text_expansion`
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==== Top level parameters for `text_expansion`
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`<sparse_vector_field>`:::
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(Required, object) The name of the field that contains the token-weight pairs the NLP model created based on the input text.
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[discrete]
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[[text-expansion-rank-feature-field-params]]
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=== Top level parameters for `<sparse_vector_field>`
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==== Top level parameters for `<sparse_vector_field>`
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`model_id`::::
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(Required, string) The ID of the model to use to convert the query text into token-weight pairs.
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@ -84,7 +84,7 @@ NOTE: The default values for `tokens_freq_ratio_threshold` and `tokens_weight_th
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[discrete]
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[[text-expansion-query-example]]
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=== Example ELSER query
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==== Example ELSER query
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The following is an example of the `text_expansion` query that references the ELSER model to perform semantic search.
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For a more detailed description of how to perform semantic search by using ELSER and the `text_expansion` query, refer to <<semantic-search-elser,this tutorial>>.
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@ -208,7 +208,7 @@ GET my-index/_search
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[discrete]
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[[text-expansion-query-with-pruning-config-and-rescore-example]]
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=== Example ELSER query with pruning configuration and rescore
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==== Example ELSER query with pruning configuration and rescore
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The following is an extension to the above example that adds a preview:[] pruning configuration to the `text_expansion` query.
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The pruning configuration identifies non-significant tokens to prune from the query in order to improve query performance.
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37
docs/reference/query-dsl/vector-queries.asciidoc
Normal file
37
docs/reference/query-dsl/vector-queries.asciidoc
Normal file
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@ -0,0 +1,37 @@
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[[vector-queries]]
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== Vector queries
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Vector queries are specialized queries that work on vector fields to efficiently perform <<semantic-search,semantic search>>.
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<<query-dsl-knn-query,`knn` query>>::
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A query that finds the _k_ nearest vectors to a query vector for <<dense-vector,`dense_vector`>> fields, as measured by a similarity metric.
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<<query-dsl-sparse-vector-query,`sparse_vector` query>>::
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A query used to search <<sparse-vector,`sparse_vector`>> field types.
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<<query-dsl-semantic-query,`semantic` query>>::
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A query that allows you to perform semantic search on <<semantic-text,`semantic_text`>> fields.
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[discrete]
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=== Deprecated vector queries
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The following queries have been deprecated and will be removed in the near future.
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Use the <<query-dsl-sparse-vector-query,`sparse_vector` query>> query instead.
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<<query-dsl-text-expansion-query,`text_expansion` query>>::
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A query that allows you to perform sparse vector search on <<sparse-vector,`sparse_vector`>> or <<rank-features,`rank_features`>> fields.
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<<query-dsl-weighted-tokens-query,`weighted_tokens` query>>::
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Allows to perform text expansion queries optimizing for performance.
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include::knn-query.asciidoc[]
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include::sparse-vector-query.asciidoc[]
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include::semantic-query.asciidoc[]
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include::text-expansion-query.asciidoc[]
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include::weighted-tokens-query.asciidoc[]
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