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

[[query-dsl-script-score-query]]
=== Script Score Query

experimental[]

The `script_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 `script_score`, you have to define a query and a script -
a function to be used to compute a new score for each document returned
by the query. For more information on scripting see
<<modules-scripting, scripting documentation>>.


Here is an example of using `script_score` to assign each matched document
a score equal to the number of likes divided by 10:

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

==== Accessing the score of a document within 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.


==== Predefined functions within a Painless script
You can use any of the available
<<painless-api-reference, painless functions>> in the painless script.
Besides these functions, there are a number of predefined functions
that can help you with scoring. We suggest you to use them instead of
rewriting equivalent functions of your own, as these functions try
to be the most efficient by using the internal mechanisms.

===== saturation
`saturation(value,k) = value/(k + value)`

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

===== sigmoid
`sigmoid(value, k, a) = value^a/ (k^a + value^a)`

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

[[vector-functions]]
===== Functions for vector fields
These functions are used for
for <<dense-vector,`dense_vector`>>  and
<<sparse-vector,`sparse_vector`>> fields.

For dense_vector fields, `cosineSimilarity` calculates the measure of
cosine similarity between a given query vector and document vectors.

[source,js]
--------------------------------------------------
{
  "query": {
    "script_score": {
      "query": {
        "match_all": {}
      },
      "script": {
        "source": "cosineSimilarity(params.queryVector, doc['my_dense_vector'])",
        "params": {
          "queryVector": [4, 3.4, -0.2]  <1>
        }
      }
    }
  }
}
--------------------------------------------------
// NOTCONSOLE
<1> To take advantage of the script optimizations, provide a query vector as a script parameter.

Similarly, for sparse_vector fields, `cosineSimilaritySparse` calculates cosine similarity
between a given query vector and document vectors.

[source,js]
--------------------------------------------------
{
  "query": {
    "script_score": {
      "query": {
        "match_all": {}
      },
      "script": {
        "source": "cosineSimilaritySparse(params.queryVector, doc['my_sparse_vector'])",
        "params": {
          "queryVector": {"2": 0.5, "10" : 111.3, "50": -1.3, "113": 14.8, "4545": 156.0}
        }
      }
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

For dense_vector fields, `dotProduct` calculates the measure of
dot product between a given query vector and document vectors.

[source,js]
--------------------------------------------------
{
  "query": {
    "script_score": {
      "query": {
        "match_all": {}
      },
      "script": {
        "source": "dotProduct(params.queryVector, doc['my_dense_vector'])",
        "params": {
          "queryVector": [4, 3.4, -0.2]
        }
      }
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

Similarly, for sparse_vector fields, `dotProductSparse` calculates dot product
between a given query vector and document vectors.

[source,js]
--------------------------------------------------
{
  "query": {
    "script_score": {
      "query": {
        "match_all": {}
      },
      "script": {
        "source": "dotProductSparse(params.queryVector, doc['my_sparse_vector'])",
        "params": {
          "queryVector": {"2": 0.5, "10" : 111.3, "50": -1.3, "113": 14.8, "4545": 156.0}
        }
      }
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

NOTE: If a document doesn't have a value for a vector field on which
a vector function is executed, 0 is returned as a result
for this document.

NOTE: If a document's dense vector field has a number of dimensions
different from the query's vector, 0 is used for missing dimensions
in the calculations of vector functions.


[[random-functions]]
===== Random functions
There are two predefined ways to produce random values:
`randomNotReproducible` and `randomReproducible`.

`randomNotReproducible()` uses `java.util.Random` class
to generate a random value of the type `long`.
The generated values are not reproducible between requests' invocations.

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


`randomReproducible(String seedValue, int seed)` produces
reproducible random values of type `long`. This function requires
more computational time and memory than the non-reproducible version.

A good candidate for the `seedValue` is document field values that
are unique across documents and already pre-calculated and preloaded
in the memory. For example, values of the document's `_seq_no` field
is a good candidate, as documents on the same shard have unique values
for the `_seq_no` field.

[source,js]
--------------------------------------------------
"script" : {
    "source" : "randomReproducible(Long.toString(doc['_seq_no'].value), 100)"
}
--------------------------------------------------
// NOTCONSOLE


A drawback of using `_seq_no` is that generated values change if
documents are updated. Another drawback is not absolute uniqueness, as
documents from different shards with the same sequence numbers
generate the same random values.

If you need random values to be distinct across different shards,
you can use a field with unique values across shards,
such as  `_id`, but watch out for the memory usage as all
these unique values need to be loaded into memory.

[source,js]
--------------------------------------------------
"script" : {
    "source" : "randomReproducible(doc['_id'].value, 100)"
}
--------------------------------------------------
// NOTCONSOLE


[[decay-functions]]
===== 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 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 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.


==== Faster alternatives
Script Score Query calculates the score for every hit (matching document).
There are faster alternative query types that can efficiently skip
non-competitive hits:

* If you want to boost documents on some static fields, use
 <<query-dsl-rank-feature-query, Rank Feature Query>>.


==== Transition from Function Score Query
We are deprecating <<query-dsl-function-score-query, Function Score>>, and
Script Score Query will be a substitute for it.

Here we describe how Function Score Query's functions can be
equivalently implemented in Script Score Query:

===== `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` 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`

Use `randomReproducible` and `randomNotReproducible` functions
as described in <<random-functions, random functions>>.


===== `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`
Script Score query has equivalent <<decay-functions, decay functions>>
that can be used in script.