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use crate::search::*;
use crate::util::*;
/// The `function_score` allows you to modify the score of documents that are retrieved by a query.
///
/// This can be useful if, for example, a score function is computationally expensive and it is
/// sufficient to compute the score on a filtered set of documents.
///
/// To use `function_score`, the user has to define a query and one or more functions, that compute
/// a new score for each document returned by the query.
///
/// To create function_score query:
/// ```
/// # use elasticsearch_dsl::queries::*;
/// # use elasticsearch_dsl::queries::params::*;
/// # let query =
/// Query::function_score()
/// .query(Query::term("test", 1))
/// .function(RandomScore::new().filter(Query::term("test", 1)).weight(2.0))
/// .function(Weight::new(2.0))
/// .max_boost(2.2)
/// .min_score(2.3)
/// .score_mode(FunctionScoreMode::Avg)
/// .boost_mode(FunctionBoostMode::Max)
/// .boost(1.1)
/// .name("test");
/// ```
/// <https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-function-score-query.html>
#[derive(Debug, Clone, PartialEq, Serialize)]
#[serde(remote = "Self")]
pub struct FunctionScoreQuery {
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
query: Option<Box<Query>>,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
functions: Vec<Function>,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
max_boost: Option<f32>,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
min_score: Option<f32>,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
score_mode: Option<FunctionScoreMode>,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
boost_mode: Option<FunctionBoostMode>,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
boost: Option<f32>,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
_name: Option<String>,
}
impl Query {
/// Creates an instance of [`FunctionScoreQuery`]
pub fn function_score() -> FunctionScoreQuery {
FunctionScoreQuery {
query: None,
functions: Default::default(),
max_boost: None,
min_score: None,
score_mode: None,
boost_mode: None,
boost: None,
_name: None,
}
}
}
impl FunctionScoreQuery {
/// Base function score query
pub fn query<T>(mut self, query: T) -> Self
where
T: Into<Option<Query>>,
{
self.query = query.into().map(Box::new);
self
}
/// Push function to the list
pub fn function<T>(mut self, function: T) -> Self
where
T: Into<Option<Function>>,
{
let function = function.into();
if let Some(function) = function {
self.functions.push(function);
}
self
}
/// Maximum score value after applying all the functions
pub fn max_boost<T>(mut self, max_boost: T) -> Self
where
T: num_traits::AsPrimitive<f32>,
{
self.max_boost = Some(max_boost.as_());
self
}
/// By default, modifying the score does not change which documents match. To exclude documents
/// that do not meet a certain score threshold the `min_score` parameter can be set to the
/// desired score threshold.
pub fn min_score<T>(mut self, min_score: T) -> Self
where
T: Into<f32>,
{
self.min_score = Some(min_score.into());
self
}
/// Each document is scored by the defined functions. The parameter `score_mode` specifies how
/// the computed scores are combined
pub fn score_mode(mut self, score_mode: FunctionScoreMode) -> Self {
self.score_mode = Some(score_mode);
self
}
/// The newly computed score is combined with the score of the query. The parameter
/// `boost_mode` defines how.
pub fn boost_mode(mut self, boost_mode: FunctionBoostMode) -> Self {
self.boost_mode = Some(boost_mode);
self
}
add_boost_and_name!();
}
impl ShouldSkip for FunctionScoreQuery {
fn should_skip(&self) -> bool {
self.query.should_skip() || self.functions.should_skip()
}
}
serialize_with_root!("function_score": FunctionScoreQuery);
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn serialization() {
assert_serialize_query(
Query::function_score().function(RandomScore::new()),
json!({
"function_score": {
"functions": [
{
"random_score": {}
}
]
}
}),
);
assert_serialize_query(
Query::function_score()
.query(Query::term("test", 1))
.function(RandomScore::new())
.function(Weight::new(2.0))
.max_boost(2.2)
.min_score(2.3)
.score_mode(FunctionScoreMode::Avg)
.boost_mode(FunctionBoostMode::Max)
.boost(1.1)
.name("test"),
json!({
"function_score": {
"query": {
"term": {
"test": {
"value": 1
}
}
},
"functions": [
{
"random_score": {}
},
{
"weight": 2.0
}
],
"max_boost": 2.2,
"min_score": 2.3,
"score_mode": "avg",
"boost_mode": "max",
"boost": 1.1,
"_name": "test"
}
}),
);
}
#[test]
fn issue_24() {
let _ = json!({
"function_score": {
"boost_mode": "replace",
"functions": [
{
"filter": { "term": { "type": "stop" } },
"field_value_factor": {
"field": "weight",
"factor": 1.0,
"missing": 1.0
},
"weight": 1.0
},
{
"filter": { "term": { "type": "address" } },
"filter": { "term": { "type": "addr" } },
"field_value_factor": {
"field": "weight",
"factor": 1.0,
"missing": 1.0
},
"weight": 1.0
},
{
"filter": { "term": { "type": "admin" } },
"field_value_factor": {
"field": "weight",
"factor": 1.0,
"missing": 1.0
},
"weight": 1.0
},
{
"filter": { "term": { "type": "poi" } },
"field_value_factor": {
"field": "weight",
"factor": 1.0,
"missing": 1.0
},
"weight": 1.0
},
{
"filter": { "term": { "type": "street" } },
"field_value_factor": {
"field": "weight",
"factor": 1.0,
"missing": 1.0
},
"weight": 1.0
}
]
}
});
let _ = Query::function_score()
.boost_mode(FunctionBoostMode::Replace)
.function(
FieldValueFactor::new("weight")
.factor(1.0)
.missing(1.0)
.weight(1.0)
.filter(Query::term("type", "stop")),
)
.function(
FieldValueFactor::new("weight")
.factor(1.0)
.missing(1.0)
.weight(1.0)
.filter(Query::terms("type", ["address", "addr"])),
)
.function(
FieldValueFactor::new("weight")
.factor(1.0)
.missing(1.0)
.weight(1.0)
.filter(Query::term("type", "admin")),
)
.function(
FieldValueFactor::new("weight")
.factor(1.0)
.missing(1.0)
.weight(1.0)
.filter(Query::term("type", "poi")),
)
.function(
FieldValueFactor::new("weight")
.factor(1.0)
.missing(1.0)
.weight(1.0)
.filter(Query::term("type", "street")),
);
}
}