Query

bool object

matches documents matching boolean combinations of other queries.

boosting object

Returns documents matching a positive query while reducing the relevance score of documents that also match a negative query.

common map string to object · min properties: 1 · max properties: 1

<string> object

combined_fields object

The combined_fields query supports searching multiple text fields as if their contents had been indexed into one combined field.

constant_score object

Wraps a filter query and returns every matching document with a relevance score equal to the boost parameter value.

dense_vector object

Scores every document that has a value for a dense_vector field, with the exact similarity between its vector and a query vector. Unlike the knn query, it doesn't use an index structure, or a cutoff based on k or num_candidates.

boost number · default: 1

Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.

_name string

field string required

The name of the dense_vector field to search against. The field can be indexed, or not indexed (index: false).

query_vector union oneOf

The query vector. It must have the same number of dimensions as the field. You must provide either query_vector or query_vector_builder, but not both.

One of: number[] or string

query_vector_builder object

The configuration to build the query vector, for example by running a text embedding model. You must provide either query_vector or query_vector_builder, but not both.

embedding object
text_embedding object
lookup object: Lookup a vector from an existing document. Must reference a dense_vector field and a single value.

similarity_function enum

The similarity used for scoring, which overrides the similarity of the field for this query. It defaults to the similarity of the field, or to cosine for a field that is not indexed. For bit fields, only l2_norm is supported. It can't be combined with quantized: true.

Supported values include:

cosine: Computes the cosine similarity. During indexing Elasticsearch automatically normalizes vectors with cosine similarity to unit length. This allows to internally use dot_product for computing similarity, which is more efficient. Original un-normalized vectors can be still accessed through scripts.

The document _score is computed as (1 + cosine(query, vector)) / 2.

The cosine similarity does not allow vectors with zero magnitude, since cosine is not defined in this case.

dot_product: Computes the dot product of two unit vectors. This option provides an optimized way to perform cosine similarity. The constraints and computed score are defined by element_type.

When element_type is float, all vectors must be unit length, including both document and query vectors.

The document _score is computed as (1 + dot_product(query, vector)) / 2.

When element_type is byte, all vectors must have the same length including both document and query vectors or results will be inaccurate.

The document _score is computed as 0.5 + (dot_product(query, vector) / (32768 * dims)) where dims is the number of dimensions per vector.

l2_norm: Computes similarity based on the L2 distance (also known as Euclidean distance) between the vectors.

The document _score is computed as 1 / (1 + l2_norm(query, vector)^2).

For bit vectors, instead of using l2_norm, the hamming distance between the vectors is used.

The _score transformation is (numBits - hamming(a, b)) / numBits.

max_inner_product: Computes the maximum inner product of two vectors. This is similar to dot_product, but doesn't require vectors to be normalized. This means that each vector’s magnitude can significantly effect the score.

The document _score is adjusted to prevent negative values. For max_inner_product values < 0, the _score is 1 / (1 + -1 * max_inner_product(query, vector)). For non-negative max_inner_product results the _score is calculated max_inner_product(query, vector) + 1.

Values: cosine dot_product l2_norm max_inner_product

quantized boolean · default: false

If false, the documents are scored with the full-precision vectors, regardless of the index options of the field. If true, the documents are scored with the quantized vectors of a quantized field, which is faster but less accurate. For a field that is not quantized, true has no effect.

dis_max object

Returns documents matching one or more wrapped queries, called query clauses or clauses. If a returned document matches multiple query clauses, the dis_max query assigns the document the highest relevance score from any matching clause, plus a tie breaking increment for any additional matching subqueries.

distance_feature union oneOf

Boosts the relevance score of documents closer to a provided origin date or point. For example, you can use this query to give more weight to documents closer to a certain date or location.

One of:

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
origin object required: Date or point of origin used to calculate distances. If the field value is a date or date_nanos field, the origin value must be a date. Date Math, such as now-1h, is supported. If the field value is a geo_point field, the origin value must be a geopoint.
pivot object required: Distance from the origin at which relevance scores receive half of the boost value. If the field value is a date or date_nanos field, the pivot value must be a time unit, such as 1h or 10d. If the field value is a geo_point field, the pivot value must be a distance unit, such as 1km or 12m.
field string required: Name of the field used to calculate distances. This field must meet the following criteria: be a date, date_nanos or geo_point field; have an index mapping parameter value of true, which is the default; have an doc_values mapping parameter value of true, which is the default.

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
origin union oneOf required: Date or point of origin used to calculate distances. If the field value is a date or date_nanos field, the origin value must be a date. Date Math, such as now-1h, is supported. If the field value is a geo_point field, the origin value must be a geopoint.; One of:
pivot string required: Distance from the origin at which relevance scores receive half of the boost value. If the field value is a date or date_nanos field, the pivot value must be a time unit, such as 1h or 10d. If the field value is a geo_point field, the pivot value must be a distance unit, such as 1km or 12m.
field string required: Name of the field used to calculate distances. This field must meet the following criteria: be a date, date_nanos or geo_point field; have an index mapping parameter value of true, which is the default; have an doc_values mapping parameter value of true, which is the default.

exists object

Returns documents that contain an indexed value for a field.

function_score object

The function_score enables you to modify the score of documents that are retrieved by a query.

boost number · default: 1

Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.

_name string

boost_mode enum · default: multiply

Defines how he newly computed score is combined with the score of the query

Supported values include:

multiply: Query score and function score are multiplied
replace: Only the function score is used. The query score is ignored.
sum: Query score and function score are added
avg: Query score and function score are averaged
max: Max of query score and function score
min: Min of query score and function score

Values: multiply replace sum avg max min

functions object[]

One or more functions that compute a new score for each document returned by the query.

filter Query
weight number
exp union oneOf: Function that scores a document with a exponential decay, depending on the distance of a numeric field value of the document from an origin.; One of:
gauss union oneOf: Function that scores a document with a normal decay, depending on the distance of a numeric field value of the document from an origin.; One of:
linear union oneOf: Function that scores a document with a linear decay, depending on the distance of a numeric field value of the document from an origin.; One of:
field_value_factor object: Function allows you to use a field from a document to influence the score. It’s similar to using the script_score function, however, it avoids the overhead of scripting.
random_score object: Generates scores that are uniformly distributed from 0 up to but not including 1. In case you want scores to be reproducible, it is possible to provide a seed and field.
script_score object: Enables you to wrap another query and customize the scoring of it optionally with a computation derived from other numeric field values in the doc using a script expression.

max_boost number

Restricts the new score to not exceed the provided limit.

min_score number

Excludes documents that do not meet the provided score threshold.

query Query

A query that determines the documents for which a new score is computed.

score_mode enum · default: multiply

Specifies how the computed scores are combined

Supported values include:

multiply: Scores are multiplied.
sum: Scores are summed.
avg: Scores are averaged.
first: The first function that has a matching filter is applied.
max: Maximum score is used.
min: Minimum score is used.

Values: multiply sum avg first max min

fuzzy map string to object · min properties: 1 · max properties: 1

Returns documents that contain terms similar to the search term, as measured by a Levenshtein edit distance.

<string> object

geo_bounding_box object

Matches geo_point and geo_shape values that intersect a bounding box.

geo_distance object

Matches geo_point and geo_shape values within a given distance of a geopoint.

geo_grid map string to object · min properties: 1 · max properties: 1

Matches geo_point and geo_shape values that intersect a grid cell from a GeoGrid aggregation.

<string> object

geo_polygon object

geo_shape object

Filter documents indexed using either the geo_shape or the geo_point type.

has_child object

Returns parent documents whose joined child documents match a provided query.

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
ignore_unmapped boolean · default: false: Indicates whether to ignore an unmapped type and not return any documents instead of an error.
inner_hits object: If defined, each search hit will contain inner hits.
max_children number: Maximum number of child documents that match the query allowed for a returned parent document. If the parent document exceeds this limit, it is excluded from the search results.
min_children number: Minimum number of child documents that match the query required to match the query for a returned parent document. If the parent document does not meet this limit, it is excluded from the search results.
query Query required: Query you wish to run on child documents of the type field. If a child document matches the search, the query returns the parent document.
score_mode enum · default: 'none': Indicates how scores for matching child documents affect the root parent document’s relevance score.; Values: none avg sum max min
type string required: Name of the child relationship mapped for the join field.

has_parent object

Returns child documents whose joined parent document matches a provided query.

ids object

Returns documents based on their IDs. This query uses document IDs stored in the _id field.

intervals map string to object · min properties: 1 · max properties: 1

Returns documents based on the order and proximity of matching terms.

<string> object

knn object

Finds the k nearest vectors to a query vector, as measured by a similarity metric. knn query finds nearest vectors through approximate search on indexed dense_vectors.

kql object

Returns documents matching a provided Kibana Query Language (KQL) expression. The expression is parsed and rewritten into standard Query DSL.

match map string to object · min properties: 1 · max properties: 1

Returns documents that match a provided text, number, date or boolean value. The provided text is analyzed before matching.

<string> object

match_all object

Matches all documents, giving them all a _score of 1.0.

match_bool_prefix map string to object · min properties: 1 · max properties: 1

Analyzes its input and constructs a bool query from the terms. Each term except the last is used in a term query. The last term is used in a prefix query.

<string> object

match_none object

Matches no documents.

match_phrase map string to object · min properties: 1 · max properties: 1

Analyzes the text and creates a phrase query out of the analyzed text.

<string> object

match_phrase_prefix map string to object · min properties: 1 · max properties: 1

Returns documents that contain the words of a provided text, in the same order as provided. The last term of the provided text is treated as a prefix, matching any words that begin with that term.

<string> object

more_like_this object

Returns documents that are "like" a given set of documents.

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
analyzer string: The analyzer that is used to analyze the free form text. Defaults to the analyzer associated with the first field in fields.
boost_terms number · default: 0: Each term in the formed query could be further boosted by their tf-idf score. This sets the boost factor to use when using this feature. Defaults to deactivated (0).
fail_on_unsupported_field boolean · default: true: Controls whether the query should fail (throw an exception) if any of the specified fields are not of the supported types (text or keyword).
fields string[]: A list of fields to fetch and analyze the text from. Defaults to the index.query.default_field index setting, which has a default value of *.
include boolean · default: false: Specifies whether the input documents should also be included in the search results returned.
like union oneOf required: Specifies free form text and/or a single or multiple documents for which you want to find similar documents.
max_doc_freq number: The maximum document frequency above which the terms are ignored from the input document.
max_query_terms number · default: 25: The maximum number of query terms that can be selected.
max_word_length number · default: 0: The maximum word length above which the terms are ignored. Defaults to unbounded (0).
min_doc_freq number · default: 5: The minimum document frequency below which the terms are ignored from the input document.
minimum_should_match number | string: After the disjunctive query has been formed, this parameter controls the number of terms that must match.
min_term_freq number · default: 2: The minimum term frequency below which the terms are ignored from the input document.
min_word_length number · default: 0: The minimum word length below which the terms are ignored.
routing string
stop_words union oneOf: An array of stop words. Any word in this set is ignored.; Values: _arabic_ _armenian_ _basque_ _bengali_ _brazilian_ _bulgarian_ _catalan_ _cjk_ _czech_ _danish_ _dutch_ _english_
unlike union oneOf: Used in combination with like to exclude documents that match a set of terms.
version number
version_type enum · default: 'internal': Supported values include:

internal: Use internal versioning that starts at 1 and increments with each update or delete.

external: Only index the document if the specified version is strictly higher than the version of the stored document or if there is no existing document.

external_gte: Only index the document if the specified version is equal or higher than the version of the stored document or if there is no existing document. NOTE: The external_gte version type is meant for special use cases and should be used with care. If used incorrectly, it can result in loss of data.; Values: internal external external_gte

multi_match object

Enables you to search for a provided text, number, date or boolean value across multiple fields. The provided text is analyzed before matching.

boost number · default: 1

Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.

_name string

analyzer string

Analyzer used to convert the text in the query value into tokens.

auto_generate_synonyms_phrase_query boolean · default: true

If true, match phrase queries are automatically created for multi-term synonyms.

cutoff_frequency number

fields union oneOf

The fields to be queried. Defaults to the index.query.default_field index settings, which in turn defaults to *.

One of: string[] or string

fuzziness number | string

Maximum edit distance allowed for matching.

fuzzy_rewrite string

Method used to rewrite the query.

fuzzy_transpositions boolean · default: true

If true, edits for fuzzy matching include transpositions of two adjacent characters (for example, ab to ba). Can be applied to the term subqueries constructed for all terms but the final term.

lenient boolean · default: false

If true, format-based errors, such as providing a text query value for a numeric field, are ignored.

max_expansions number · default: 50

Maximum number of terms to which the query will expand.

minimum_should_match number | string

Minimum number of clauses that must match for a document to be returned.

operator enum · default: 'or'

Boolean logic used to interpret text in the query value.

Values: and AND or OR

prefix_length number · default: 0

Number of beginning characters left unchanged for fuzzy matching.

query string required

Text, number, boolean value or date you wish to find in the provided field.

slop number · default: 0

Maximum number of positions allowed between matching tokens.

tie_breaker number · default: 0

Determines how scores for each per-term blended query and scores across groups are combined.

type enum · default: 'best_fields'

How the multi_match query is executed internally.

Supported values include:

best_fields: Finds documents that match any field, but uses the _score from the best field.
most_fields: Finds documents that match any field and combines the _score from each field.
cross_fields: Treats fields with the same analyzer as though they were one big field. Looks for each word in any field.
phrase: Runs a match_phrase query on each field and uses the _score from the best field.
phrase_prefix: Runs a match_phrase_prefix query on each field and uses the _score from the best field.
bool_prefix: Creates a match_bool_prefix query on each field and combines the _score from each field.

Values: best_fields most_fields cross_fields phrase phrase_prefix bool_prefix

zero_terms_query enum · default: 'none'

Indicates whether no documents are returned if the analyzer removes all tokens, such as when using a stop filter.

Supported values include:

all: Returns all documents, similar to a match_all query.
none: No documents are returned if the analyzer removes all tokens.

Values: all none

nested object

Wraps another query to search nested fields. If an object matches the search, the nested query returns the root parent document.

parent_id object

Returns child documents joined to a specific parent document.

percolate object

Matches queries stored in an index.

pinned object

Promotes selected documents to rank higher than those matching a given query.

prefix map string to object · min properties: 1 · max properties: 1

Returns documents that contain a specific prefix in a provided field.

<string> object

query_string object

Returns documents based on a provided query string, using a parser with a strict syntax.

boost number · default: 1

Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.

_name string

allow_leading_wildcard boolean · default: true

If true, the wildcard characters * and ? are allowed as the first character of the query string.

analyzer string

Analyzer used to convert text in the query string into tokens.

analyze_wildcard boolean · default: false

If true, the query attempts to analyze wildcard terms in the query string.

auto_generate_synonyms_phrase_query boolean · default: true

If true, match phrase queries are automatically created for multi-term synonyms.

default_field string

Default field to search if no field is provided in the query string. Supports wildcards (*). Defaults to the index.query.default_field index setting, which has a default value of *.

default_operator enum · default: 'or'

Default boolean logic used to interpret text in the query string if no operators are specified.

Values: and AND or OR

enable_position_increments boolean · default: true

If true, enable position increments in queries constructed from a query_string search.

escape boolean · default: false

fields string[]

Array of fields to search. Supports wildcards (*).

fuzziness number | string

Maximum edit distance allowed for fuzzy matching.

fuzzy_max_expansions number · default: 50

Maximum number of terms to which the query expands for fuzzy matching.

fuzzy_prefix_length number · default: 0

Number of beginning characters left unchanged for fuzzy matching.

fuzzy_rewrite string

Method used to rewrite the query.

fuzzy_transpositions boolean · default: true

If true, edits for fuzzy matching include transpositions of two adjacent characters (for example, ab to ba).

lenient boolean · default: false

If true, format-based errors, such as providing a text value for a numeric field, are ignored.

max_determinized_states number · default: 10000

Maximum number of automaton states required for the query.

minimum_should_match number | string

Minimum number of clauses that must match for a document to be returned.

phrase_slop number · default: 0

Maximum number of positions allowed between matching tokens for phrases.

query string required

Query string you wish to parse and use for search.

quote_analyzer string

Analyzer used to convert quoted text in the query string into tokens. For quoted text, this parameter overrides the analyzer specified in the analyzer parameter.

quote_field_suffix string

Suffix appended to quoted text in the query string. You can use this suffix to use a different analysis method for exact matches.

rewrite string

Method used to rewrite the query.

tie_breaker number

How to combine the queries generated from the individual search terms in the resulting dis_max query.

time_zone string

Coordinated Universal Time (UTC) offset or IANA time zone used to convert date values in the query string to UTC.

type enum · default: 'best_fields'

Determines how the query matches and scores documents.

Supported values include:

best_fields: Finds documents that match any field, but uses the _score from the best field.
most_fields: Finds documents that match any field and combines the _score from each field.
cross_fields: Treats fields with the same analyzer as though they were one big field. Looks for each word in any field.
phrase: Runs a match_phrase query on each field and uses the _score from the best field.
phrase_prefix: Runs a match_phrase_prefix query on each field and uses the _score from the best field.
bool_prefix: Creates a match_bool_prefix query on each field and combines the _score from each field.

Values: best_fields most_fields cross_fields phrase phrase_prefix bool_prefix

range map string to oneOf · min properties: 1 · max properties: 1

Returns documents that contain terms within a provided range.

<string> union oneOf

rank_feature object

Boosts the relevance score of documents based on the numeric value of a rank_feature or rank_features field.

regexp map string to object · min properties: 1 · max properties: 1

Returns documents that contain terms matching a regular expression.

<string> object

rule object

script object

Filters documents based on a provided script. The script query is typically used in a filter context.

script_score object

Uses a script to provide a custom score for returned documents.

semantic object

A semantic query to semantic_text field types

shape object

Queries documents that contain fields indexed using the shape type.

simple_query_string object

Returns documents based on a provided query string, using a parser with a limited but fault-tolerant syntax.

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
analyzer string: Analyzer used to convert text in the query string into tokens.
analyze_wildcard boolean · default: false: If true, the query attempts to analyze wildcard terms in the query string.
auto_generate_synonyms_phrase_query boolean · default: true: If true, the parser creates a match_phrase query for each multi-position token.
default_operator enum · default: 'or': Default boolean logic used to interpret text in the query string if no operators are specified.; Values: and AND or OR
fields string[]: Array of fields you wish to search. Accepts wildcard expressions. You also can boost relevance scores for matches to particular fields using a caret (^) notation. Defaults to the index.query.default_field index setting, which has a default value of *.
flags union · default: ALL: List of enabled operators for the simple query string syntax.; Values: NONE AND NOT OR PREFIX PHRASE PRECEDENCE ESCAPE WHITESPACE FUZZY NEAR SLOP ALL
fuzzy_max_expansions number · default: 50: Maximum number of terms to which the query expands for fuzzy matching.
fuzzy_prefix_length number · default: 0: Number of beginning characters left unchanged for fuzzy matching.
fuzzy_transpositions boolean: If true, edits for fuzzy matching include transpositions of two adjacent characters (for example, ab to ba).
lenient boolean · default: false: If true, format-based errors, such as providing a text value for a numeric field, are ignored.
minimum_should_match number | string: Minimum number of clauses that must match for a document to be returned.
query string required: Query string in the simple query string syntax you wish to parse and use for search.
quote_field_suffix string: Suffix appended to quoted text in the query string.

span_containing object

Returns matches which enclose another span query.

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
big object required: Can be any span query. Matching spans from big that contain matches from little are returned.
little object required: Can be any span query. Matching spans from big that contain matches from little are returned.; Same fields as big in span_containing

span_field_masking object

Wrapper to allow span queries to participate in composite single-field span queries by lying about their search field.

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
field string required
query object required

span_first object

Matches spans near the beginning of a field.

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
end number required: Controls the maximum end position permitted in a match.
match object required: Can be any other span type query.

span_multi object

Allows you to wrap a multi term query (one of wildcard, fuzzy, prefix, range, or regexp query) as a span query, so it can be nested.

span_near object

Matches spans which are near one another. You can specify slop, the maximum number of intervening unmatched positions, as well as whether matches are required to be in-order.

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
clauses object[] required: Array of one or more other span type queries.
in_order boolean: Controls whether matches are required to be in-order.
slop number: Controls the maximum number of intervening unmatched positions permitted.

span_not object

Removes matches which overlap with another span query or which are within x tokens before (controlled by the parameter pre) or y tokens after (controlled by the parameter post) another span query.

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
dist number: The number of tokens from within the include span that can’t have overlap with the exclude span. Equivalent to setting both pre and post.
exclude object required: Span query whose matches must not overlap those returned.
include object required: Span query whose matches are filtered.; Same fields as exclude in span_not
post number · default: 0: The number of tokens after the include span that can’t have overlap with the exclude span.
pre number · default: 0: The number of tokens before the include span that can’t have overlap with the exclude span.

span_or object

Matches the union of its span clauses.

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
clauses object[] required: Array of one or more other span type queries.

span_term map string to object · min properties: 1 · max properties: 1

Matches spans containing a term.

<string> object

span_within object

Returns matches which are enclosed inside another span query.

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
big object required: Can be any span query. Matching spans from little that are enclosed within big are returned.
little object required: Can be any span query. Matching spans from little that are enclosed within big are returned.; Same fields as big in span_within

sparse_vector object

Using input query vectors or a natural language processing model to convert a query into a list of token-weight pairs, queries against a sparse vector field.

boost number · default: 1: Floating point number used to decrease or increase the relevance scores of the query. Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.
_name string
field string required: The name of the field that contains the token-weight pairs to be searched against. This field must be a mapped sparse_vector field.
query string: The query text you want to use for search. If inference_id is specified, query must also be specified.
prune boolean: Whether to perform pruning, omitting the non-significant tokens from the query to improve query performance. If prune is true but the pruning_config is not specified, pruning will occur but default values will be used. Default: false
pruning_config object: Optional pruning configuration. If enabled, this will omit non-significant tokens from the query in order to improve query performance. This is only used if prune is set to true. If prune is set to true but pruning_config is not specified, default values will be used.
query_vector map string to number: Dictionary of precomputed sparse vectors and their associated weights. Only one of inference_id or query_vector may be supplied in a request.
inference_id string: The inference ID to use to convert the query text into token-weight pairs. It must be the same inference ID that was used to create the tokens from the input text. Only one of inference_id and query_vector is allowed. If inference_id is specified, query must also be specified. Only one of inference_id or query_vector may be supplied in a request.

term map string to object · min properties: 1 · max properties: 1

Returns documents that contain an exact term in a provided field. To return a document, the query term must exactly match the queried field's value, including whitespace and capitalization.

<string> object

terms object

Returns documents that contain one or more exact terms in a provided field. To return a document, one or more terms must exactly match a field value, including whitespace and capitalization.

terms_set map string to object · min properties: 1 · max properties: 1

Returns documents that contain a minimum number of exact terms in a provided field. To return a document, a required number of terms must exactly match the field values, including whitespace and capitalization.

<string> object

text_expansion map string to object · min properties: 1 · max properties: 1

Uses a natural language processing model to convert the query text into a list of token-weight pairs which are then used in a query against a sparse vector or rank features field.

<string> object

weighted_tokens map string to object · min properties: 1 · max properties: 1

Supports returning text_expansion query results by sending in precomputed tokens with the query.

<string> object

wildcard map string to object · min properties: 1 · max properties: 1

Returns documents that contain terms matching a wildcard pattern.

<string> object

wrapper object

A query that accepts any other query as base64 encoded string.

type object

Description

Properties