OpenSearch/docs/reference/query-dsl/script-score-query.asciidoc

[[query-dsl-script-score-query]]
=== Script score query
++++
<titleabbrev>Script score</titleabbrev>
++++

Uses a <<modules-scripting,script>> to provide a custom score for returned
documents.

The `script_score` query is useful if, for example, a scoring function is expensive and you only need to calculate the score of a filtered set of documents.


[[script-score-query-ex-request]]
==== Example request
The following `script_score` query assigns each returned document a score equal to the `likes` field value divided by `10`.

[source,console]
--------------------------------------------------
GET /_search
{
    "query" : {
        "script_score" : {
            "query" : {
                "match": { "message": "elasticsearch" }
            },
            "script" : {
                "source" : "doc['likes'].value / 10 "
            }
        }
     }
}
--------------------------------------------------


[[script-score-top-level-params]]
==== Top-level parameters for `script_score`
`query`::
(Required, query object) Query used to return documents.

`script`::
+
--
(Required, <<modules-scripting-using,script object>>) Script used to compute the score of documents returned by the `query`.

IMPORTANT: Final relevance scores from the `script_score` query cannot be
negative. To support certain search optimizations, Lucene requires
scores be positive or `0`.
--

`min_score`::
(Optional, float) Documents with a <<relevance-scores,relevance score>> lower
than this floating point number are excluded from the search results.


[[script-score-query-notes]]
==== Notes

[[script-score-access-scores]]
===== Use relevance scores in a script

Within a script, you can
{ref}/modules-scripting-fields.html#scripting-score[access] 
the `_score` variable which represents the current relevance score of a
document.

[[script-score-predefined-functions]]
===== Predefined functions
You can use any of the available {painless}/painless-contexts.html[painless
functions] in your `script`. You can also use the following predefined functions
to customize scoring:

* <<script-score-saturation>>
* <<script-score-sigmoid>>
* <<random-score-function>>
* <<decay-functions-numeric-fields>>
* <<decay-functions-geo-fields>>
* <<decay-functions-date-fields>>
* <<script-score-functions-vector-fields>>

We suggest using these predefined functions instead of writing your own.
These functions take advantage of efficiencies from {es}' internal mechanisms.

[[script-score-saturation]]
====== Saturation
`saturation(value,k) = value/(k + value)`

[source,js]
--------------------------------------------------
"script" : {
    "source" : "saturation(doc['likes'].value, 1)"
}
--------------------------------------------------
// NOTCONSOLE

[[script-score-sigmoid]]
====== Sigmoid
`sigmoid(value, k, a) = value^a/ (k^a + value^a)`

[source,js]
--------------------------------------------------
"script" : {
    "source" : "sigmoid(doc['likes'].value, 2, 1)"
}
--------------------------------------------------
// NOTCONSOLE

[[random-score-function]]
====== Random score function
`random_score` function generates scores that are uniformly distributed
from 0 up to but not including 1.

`randomScore` function has the following syntax:
`randomScore(<seed>, <fieldName>)`.
It has a required parameter - `seed` as an integer value,
and an optional parameter - `fieldName` as a string value.

[source,js]
--------------------------------------------------
"script" : {
    "source" : "randomScore(100, '_seq_no')"
}
--------------------------------------------------
// NOTCONSOLE

If the `fieldName` parameter is omitted, the internal Lucene
document ids will be used as a source of randomness. This is very efficient,
but unfortunately not reproducible since documents might be renumbered
by merges.

[source,js]
--------------------------------------------------
"script" : {
    "source" : "randomScore(100)"
}
--------------------------------------------------
// NOTCONSOLE

Note that documents that are within the same shard and have the
same value for field will get the same score, so it is usually desirable
to use a field that has unique values for all documents across a shard.
A good default choice might be to use the `_seq_no`
field, whose only drawback is that scores will change if the document is
updated since update operations also update the value of the `_seq_no` field.


[[decay-functions-numeric-fields]]
====== Decay functions for numeric fields
You can read more about decay functions 
{ref}/query-dsl-function-score-query.html#function-decay[here].

* `double decayNumericLinear(double origin, double scale, double offset, double decay, double docValue)`
* `double decayNumericExp(double origin, double scale, double offset, double decay, double docValue)`
* `double decayNumericGauss(double origin, double scale, double offset, double decay, double docValue)`

[source,js]
--------------------------------------------------
"script" : {
    "source" : "decayNumericLinear(params.origin, params.scale, params.offset, params.decay, doc['dval'].value)",
    "params": { <1>
        "origin": 20,
        "scale": 10,
        "decay" : 0.5,
        "offset" : 0
    }
}
--------------------------------------------------
// NOTCONSOLE
<1> Using `params` allows to compile the script only once, even if params change.

[[decay-functions-geo-fields]]
====== Decay functions for geo fields

* `double decayGeoLinear(String originStr, String scaleStr, String offsetStr, double decay, GeoPoint docValue)`

* `double decayGeoExp(String originStr, String scaleStr, String offsetStr, double decay, GeoPoint docValue)`

* `double decayGeoGauss(String originStr, String scaleStr, String offsetStr, double decay, GeoPoint docValue)`

[source,js]
--------------------------------------------------
"script" : {
    "source" : "decayGeoExp(params.origin, params.scale, params.offset, params.decay, doc['location'].value)",
    "params": {
        "origin": "40, -70.12",
        "scale": "200km",
        "offset": "0km",
        "decay" : 0.2
    }
}
--------------------------------------------------
// NOTCONSOLE

[[decay-functions-date-fields]]
====== Decay functions for date fields

* `double decayDateLinear(String originStr, String scaleStr, String offsetStr, double decay, JodaCompatibleZonedDateTime docValueDate)`

* `double decayDateExp(String originStr, String scaleStr, String offsetStr, double decay, JodaCompatibleZonedDateTime docValueDate)`

* `double decayDateGauss(String originStr, String scaleStr, String offsetStr, double decay, JodaCompatibleZonedDateTime docValueDate)`

[source,js]
--------------------------------------------------
"script" : {
    "source" : "decayDateGauss(params.origin, params.scale, params.offset, params.decay, doc['date'].value)",
    "params": {
        "origin": "2008-01-01T01:00:00Z",
        "scale": "1h",
        "offset" : "0",
        "decay" : 0.5
    }
}
--------------------------------------------------
// NOTCONSOLE

NOTE: Decay functions on dates are limited to dates in the default format
and default time zone. Also calculations with `now` are not supported.

[[script-score-functions-vector-fields]]
====== Functions for vector fields
<<vector-functions, Functions for vector fields>> are accessible through
`script_score` query.

[[script-score-faster-alt]]
===== Faster alternatives
The `script_score` query calculates the score for
every matching document, or hit. There are faster alternative query types that
can efficiently skip non-competitive hits:

* If you want to boost documents on some static fields, use the 
 <<query-dsl-rank-feature-query, `rank_feature`>> query.
 * If you want to boost documents closer to a date or geographic point, use the
 <<query-dsl-distance-feature-query, `distance_feature`>> query.

[[script-score-function-score-transition]]
===== Transition from the function score query
We are deprecating the <<query-dsl-function-score-query, `function_score`>>
query. We recommend using the `script_score` query instead.

You can implement the following functions from the `function_score` query using
the `script_score` query:

* <<script-score>>
* <<weight>>
* <<random-score>>
* <<field-value-factor>>
* <<decay-functions>>

[[script-score]]
====== `script_score`
What you used in `script_score` of the Function Score query, you
can copy into the Script Score query. No changes here.

[[weight]]
====== `weight`
`weight` function can be implemented in the Script Score query through
the following script:

[source,js]
--------------------------------------------------
"script" : {
    "source" : "params.weight * _score",
    "params": {
        "weight": 2
    }
}
--------------------------------------------------
// NOTCONSOLE

[[random-score]]
====== `random_score`

Use `randomScore` function
as described in <<random-score-function, random score function>>.

[[field-value-factor]]
====== `field_value_factor`
`field_value_factor` function can be easily implemented through script:

[source,js]
--------------------------------------------------
"script" : {
    "source" : "Math.log10(doc['field'].value * params.factor)",
    params" : {
        "factor" : 5
    }
}
--------------------------------------------------
// NOTCONSOLE


For checking if a document has a missing value, you can use
`doc['field'].size() == 0`. For example, this script will use
a value `1` if a document doesn't have a field `field`:

[source,js]
--------------------------------------------------
"script" : {
    "source" : "Math.log10((doc['field'].size() == 0 ? 1 : doc['field'].value()) * params.factor)",
    params" : {
        "factor" : 5
    }
}
--------------------------------------------------
// NOTCONSOLE

This table lists how `field_value_factor` modifiers can be implemented
through a script:

[cols="<,<",options="header",]
|=======================================================================
| Modifier | Implementation in Script Score

| `none` | -
| `log` |  `Math.log10(doc['f'].value)`
| `log1p` | `Math.log10(doc['f'].value + 1)`
| `log2p` | `Math.log10(doc['f'].value + 2)`
| `ln` | `Math.log(doc['f'].value)`
| `ln1p` | `Math.log(doc['f'].value + 1)`
| `ln2p` | `Math.log(doc['f'].value + 2)`
| `square` | `Math.pow(doc['f'].value, 2)`
| `sqrt` | `Math.sqrt(doc['f'].value)`
| `reciprocal` | `1.0 / doc['f'].value`
|=======================================================================

[[decay-functions]]
====== `decay` functions
The `script_score` query has equivalent <<decay-functions, decay functions>>
that can be used in script.

include::{es-repo-dir}/vectors/vector-functions.asciidoc[]

[[score-explanation]]
====== Explain request
Using an <<search-explain, explain request>> provides an explanation of how the parts of a score were computed. The `script_score` query can add its own explanation by setting the `explanation` parameter:

[source,console]
--------------------------------------------------
GET /twitter/_explain/0
{
    "query" : {
        "script_score" : {
            "query" : {
                "match": { "message": "elasticsearch" }
            },
            "script" : {
                "source" : """
                  long likes = doc['likes'].value;
                  double normalizedLikes = likes / 10;
                  if (explanation != null) {
                    explanation.set('normalized likes = likes / 10 = ' + likes + ' / 10 = ' + normalizedLikes);
                  }
                  return normalizedLikes;
                """
            }
        }
     }
}
--------------------------------------------------
// TEST[setup:twitter]

Note that the `explanation` will be null when using in a normal `_search` request, so having a conditional guard is best practice.