Struct elasticsearch_dsl::search::queries::specialized::MoreLikeThisQuery

pub struct MoreLikeThisQuery { /* fields omitted */ }

The More Like This Query finds documents that are “like” a given set of documents. In order to do so, MLT selects a set of representative terms of these input documents, forms a query using these terms, executes the query and returns the results. The user controls the input documents, how the terms should be selected and how the query is formed.

The simplest use case consists of asking for documents that are similar to a provided piece of text. Here, we are asking for all movies that have some text similar to “Once upon a time” in their “title” and in their “description” fields, limiting the number of selected terms to 12.

A more complicated use case consists of mixing texts with documents already existing in the index. In this case, the syntax to specify a document is similar to the one used in the Multi GET API.

Finally, users can mix some texts, a chosen set of documents but also provide documents not necessarily present in the index. To provide documents not present in the index, the syntax is similar to artificial documents.

How it Works Suppose we wanted to find all documents similar to a given input document. Obviously, the input document itself should be its best match for that type of query. And the reason would be mostly, according to Lucene scoring formula, due to the terms with the highest tf-idf. Therefore, the terms of the input document that have the highest tf-idf are good representatives of that document, and could be used within a disjunctive query (or OR) to retrieve similar documents. The MLT query simply extracts the text from the input document, analyzes it, usually using the same analyzer at the field, then selects the top K terms with highest tf-idf to form a disjunctive query of these terms.

To create a more_like_this query with like as a string on title field:

Query::more_like_this(["test"])
    .fields(["title"]);

To create a more_like_this query with string and document id fields on title and description with optional fields:

Query::more_like_this([Like::from(Document::new("123")), Like::from("test")])
    .fields(["title", "description"])
    .min_term_freq(1)
    .max_query_terms(12)
    .boost(1.2)
    .name("more_like_this");

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-mlt-query.html

Implementations

impl MoreLikeThisQuery

pub fn fields<I>(self, fields: I) -> Self where
    I: IntoIterator,
    I::Item: Into<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 *. The * value matches all fields eligible for term-level queries, excluding metadata fields.

pub fn unlike<I>(self, unlike: I) -> Self where
    I: IntoIterator,
    I::Item: Into<Like>, 

The unlike parameter is used in conjunction with like in order not to select terms found in a chosen set of documents. In other words, we could ask for documents like: “Apple”, but unlike: “cake crumble tree”. The syntax is the same as like.

pub fn max_query_terms(self, max_query_terms: impl Into<i64>) -> Self

The maximum number of query terms that will be selected. Increasing this value gives greater accuracy at the expense of query execution speed. Defaults to 25.

pub fn min_term_freq(self, min_term_freq: impl Into<i64>) -> Self

The minimum term frequency below which the terms will be ignored from the input document. Defaults to 2.

pub fn min_doc_freq(self, min_doc_freq: impl Into<i64>) -> Self

The minimum document frequency below which the terms will be ignored from the input document. Defaults to 5.

pub fn max_doc_freq(self, max_doc_freq: impl Into<i64>) -> Self

The maximum document frequency above which the terms will be ignored from the input document. This could be useful in order to ignore highly frequent words such as stop words. Defaults to unbounded (Integer.MAX_VALUE, which is 2^31-1 or 2147483647).

pub fn min_word_length(self, min_word_length: impl Into<i64>) -> Self

The minimum word length below which the terms will be ignored. Defaults to 0.

pub fn max_word_length(self, max_word_length: impl Into<i64>) -> Self

The maximum word length above which the terms will be ignored. Defaults to unbounded (0).

pub fn stop_words<I>(self, stop_words: I) -> Self where
    I: IntoIterator,
    I::Item: Into<String>, 

An array of stop words. Any word in this set is considered “uninteresting” and ignored. If the analyzer allows for stop words, you might want to tell MLT to explicitly ignore them, as for the purposes of document similarity it seems reasonable to assume that “a stop word is never interesting”.

pub fn analyzer(self, analyzer: impl Into<String>) -> Self

The analyzer that is used to analyze the free form text. Defaults to the analyzer associated with the first field in fields.

pub fn minimum_should_match(
    self,
    minimum_should_match: impl Into<String>
) -> Self

After the disjunctive query has been formed, this parameter controls the number of terms that must match. The syntax is the same as the minimum should match. (Defaults to “30%”).

pub fn fail_on_unsupported_field(
    self,
    fail_on_unsupported_field: impl Into<bool>
) -> Self

Controls whether the query should fail (throw an exception) if any of the specified fields are not of the supported types (text or keyword). Set this to false to ignore the field and continue processing. Defaults to true.

pub fn boost_terms(self, boost_terms: impl Into<f64>) -> Self

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). Any other positive value activates terms boosting with the given boost factor.

pub fn include(self, include: impl Into<bool>) -> Self

Specifies whether the input documents should also be included in the search results returned. Defaults to false.

pub fn boost(self, boost: impl Into<Boost>) -> Self

Floating point number used to decrease or increase the relevance scores of a query. Defaults to 1.0.

You can use the boost parameter to adjust relevance scores for searches containing two or more queries.

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.

pub fn name(self, name: impl Into<String>) -> Self

You can use named queries to track which queries matched returned documents. If named queries are used, the response includes a matched_queries property for each hit.

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-bool-query.html#named-queries

Trait Implementations

impl Clone for MoreLikeThisQuery

fn clone(&self) -> MoreLikeThisQuery

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for MoreLikeThisQuery

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl From<MoreLikeThisQuery> for Query

fn from(q: MoreLikeThisQuery) -> Self

Performs the conversion.

impl From<MoreLikeThisQuery> for Option<Query>

fn from(q: MoreLikeThisQuery) -> Self

Performs the conversion.

impl PartialEq<MoreLikeThisQuery> for MoreLikeThisQuery

fn eq(&self, other: &MoreLikeThisQuery) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &MoreLikeThisQuery) -> bool

This method tests for !=.

impl Serialize for MoreLikeThisQuery

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for MoreLikeThisQuery