---
title: Script score query
description: Uses a script to provide a custom score for returned documents. The script_score query is useful if, for example, a scoring function is expensive and...
url: https://www.elastic.co/elastic/docs-builder/docs/2322/reference/query-languages/query-dsl/query-dsl-script-score-query
---

# Script score query
Uses a [script](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/2322/explore-analyze/scripting) 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.

## Example request

The following `script_score` query assigns each returned document a score equal to the `my-int` field value divided by `10`.
```json

{
  "query": {
    "script_score": {
      "query": {
        "match": { "message": "elasticsearch" }
      },
      "script": {
        "source": "doc['my-int'].value / 10 "
      }
    }
  }
}
```


## Top-level parameters for `script_score`

<definitions>
  <definition term="query">
    (Required, query object) Query used to return documents.
  </definition>
  <definition term="script">
    (Required, [script object](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/2322/explore-analyze/scripting/modules-scripting-using)) Script used to compute the score of documents returned by the `query`.
  </definition>
</definitions>

<important>
  Final relevance scores from the `script_score` query cannot be negative. To support certain search optimizations, Lucene requires scores be positive or `0`.
</important>

<definitions>
  <definition term="min_score">
    (Optional, float) Documents with a score lower than this floating point number are excluded from search results and results collected by aggregations.
  </definition>
  <definition term="boost">
    (Optional, float) Documents' scores produced by `script` are multiplied by `boost` to produce final documents' scores. Defaults to `1.0`.
  </definition>
</definitions>


## Notes


### Use relevance scores in a script

Within a script, you can [access](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/2322/explore-analyze/scripting/modules-scripting-fields#scripting-score) the `_score` variable which represents the current relevance score of a document.

### Use term statistics in a script

Within a script, you can [access](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/2322/explore-analyze/scripting/modules-scripting-fields#scripting-term-statistics) the `_termStats` variable which provides statistical information about the terms used in the child query of the `script_score` query.

### Predefined functions

You can use any of the available [painless functions](https://www.elastic.co/elastic/docs-builder/docs/2322/reference/scripting-languages/painless/painless-contexts) in your `script`. You can also use the following predefined functions to customize scoring:
- [Saturation](#script-score-saturation)
- [Sigmoid](#script-score-sigmoid)
- [Random score function](#random-score-function)
- [Decay functions for numeric fields](#decay-functions-numeric-fields)
- [Decay functions for geo fields](#decay-functions-geo-fields)
- [Decay functions for date fields](#decay-functions-date-fields)
- [Functions for vector fields](#script-score-functions-vector-fields)

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

#### Saturation

`saturation(value,k) = value/(k + value)`
```js
"script" : {
    "source" : "saturation(doc['my-int'].value, 1)"
}
```


#### Sigmoid

`sigmoid(value, k, a) = value^a/ (k^a + value^a)`
```js
"script" : {
    "source" : "sigmoid(doc['my-int'].value, 2, 1)"
}
```


#### 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.
```js
"script" : {
    "source" : "randomScore(100, '_seq_no')"
}
```

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.
```js
"script" : {
    "source" : "randomScore(100)"
}
```

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 for numeric fields

You can read more about decay functions [here](/elastic/docs-builder/docs/2322/reference/query-languages/query-dsl/query-dsl-function-score-query#function-decay).
- `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)`

```js
"script" : {
    "source" : "decayNumericLinear(params.origin, params.scale, params.offset, params.decay, doc['dval'].value)",
    "params": { 
        "origin": 20,
        "scale": 10,
        "decay" : 0.5,
        "offset" : 0
    }
}
```


#### 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)`

```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
    }
}
```


#### 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)`

```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
    }
}
```

<note>
  Decay functions on dates are limited to dates in the default format and default time zone. Also calculations with `now` are not supported.
</note>


#### Functions for vector fields

[Functions for vector fields](#vector-functions) are accessible through `script_score` query.

### Allow expensive queries

Script score queries will not be executed if [`search.allow_expensive_queries`](/elastic/docs-builder/docs/2322/reference/query-languages/querydsl#query-dsl-allow-expensive-queries) is set to false.

### 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 [`rank_feature`](https://www.elastic.co/elastic/docs-builder/docs/2322/reference/query-languages/query-dsl/query-dsl-rank-feature-query) query.
- If you want to boost documents closer to a date or geographic point, use the [`distance_feature`](https://www.elastic.co/elastic/docs-builder/docs/2322/reference/query-languages/query-dsl/query-dsl-distance-feature-query) query.


### Transition from the function score query

We recommend using the `script_score` query instead of [`function_score`](https://www.elastic.co/elastic/docs-builder/docs/2322/reference/query-languages/query-dsl/query-dsl-function-score-query) query for the simplicity of the `script_score` query.
You can implement the following functions of the `function_score` query using the `script_score` query:
- [`script_score`](#script-score)
- [`weight`](#weight)
- [`random_score`](#random-score)
- [`field_value_factor`](#field-value-factor)
- [`decay` functions](#decay-functions)


#### `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:
```js
"script" : {
    "source" : "params.weight * _score",
    "params": {
        "weight": 2
    }
}
```


#### `random_score`

Use `randomScore` function as described in [random score function](#random-score-function).

#### `field_value_factor`

`field_value_factor` function can be easily implemented through script:
```js
"script" : {
    "source" : "Math.log10(doc['field'].value * params.factor)",
    "params" : {
        "factor" : 5
    }
}
```

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

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

| 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

The `script_score` query has equivalent [decay functions](#decay-functions-numeric-fields) that can be used in scripts.

### Functions for vector fields

<note>
  During vector functions' calculation, all matched documents are linearly scanned. Thus, expect the query time grow linearly with the number of matched documents. For this reason, we recommend to limit the number of matched documents with a `query` parameter.
</note>

This is the list of available vector functions and vector access methods:
1. [`cosineSimilarity`](#vector-functions-cosine) – calculates cosine similarity
2. [`dotProduct`](#vector-functions-dot-product) – calculates dot product
3. [`l1norm`](#vector-functions-l1) – calculates L1 distance
4. [`hamming`](#vector-functions-hamming) – calculates Hamming distance
5. [`l2norm`](#vector-functions-l2) - calculates L2 distance
6. [`doc[<field>].vectorValue`](#vector-functions-accessing-vectors) – returns a vector’s value as an array of floats
7. [`doc[<field>].magnitude`](#vector-functions-accessing-vectors) – returns a vector’s magnitude

<note>
  The `cosineSimilarity` function is not supported for `bit` vectors.
</note>

<note>
  The recommended way to access dense vectors is through the `cosineSimilarity`, `dotProduct`, `l1norm` or `l2norm` functions. Please note however, that you should call these functions only once per script. For example, don’t use these functions in a loop to calculate the similarity between a document vector and multiple other vectors. If you need that functionality, reimplement these functions yourself by [accessing vector values directly](#vector-functions-accessing-vectors).
</note>

Let’s create an index with a `dense_vector` mapping and index a couple of documents into it.
```json

{
  "mappings": {
    "properties": {
      "my_dense_vector": {
        "type": "dense_vector",
        "index": false,
        "dims": 3
      },
      "my_byte_dense_vector": {
        "type": "dense_vector",
        "index": false,
        "dims": 3,
        "element_type": "byte"
      },
      "status" : {
        "type" : "keyword"
      }
    }
  }
}


{
  "my_dense_vector": [0.5, 10, 6],
  "my_byte_dense_vector": [0, 10, 6],
  "status" : "published"
}


{
  "my_dense_vector": [-0.5, 10, 10],
  "my_byte_dense_vector": [0, 10, 10],
  "status" : "published"
}
```


#### Cosine similarity

The `cosineSimilarity` function calculates the measure of cosine similarity between a given query vector and document vectors.
```json

{
  "query": {
    "script_score": {
      "query" : {
        "bool" : {
          "filter" : {
            "term" : {
              "status" : "published" <1>
            }
          }
        }
      },
      "script": {
        "source": "cosineSimilarity(params.query_vector, 'my_dense_vector') + 1.0", <2>
        "params": {
          "query_vector": [4, 3.4, -0.2]  <3>
        }
      }
    }
  }
}
```

<note>
  If a document’s dense vector field has a number of dimensions different from the query’s vector, an error will be thrown.
</note>


#### Dot product

The `dotProduct` function calculates the measure of dot product between a given query vector and document vectors.
```json

{
  "query": {
    "script_score": {
      "query" : {
        "bool" : {
          "filter" : {
            "term" : {
              "status" : "published"
            }
          }
        }
      },
      "script": {
        "source": """
          double value = dotProduct(params.query_vector, 'my_dense_vector');
          return sigmoid(1, Math.E, -value); <1>
        """,
        "params": {
          "query_vector": [4, 3.4, -0.2]
        }
      }
    }
  }
}
```


#### L1 distance (Manhattan distance)

The `l1norm` function calculates L1 distance (Manhattan distance) between a given query vector and document vectors.
```json

{
  "query": {
    "script_score": {
      "query" : {
        "bool" : {
          "filter" : {
            "term" : {
              "status" : "published"
            }
          }
        }
      },
      "script": {
        "source": "1 / (1 + l1norm(params.queryVector, 'my_dense_vector'))", <1>
        "params": {
          "queryVector": [4, 3.4, -0.2]
        }
      }
    }
  }
}
```


#### Hamming distance

The `hamming` function calculates [Hamming distance](https://en.wikipedia.org/wiki/Hamming_distance) between a given query vector and document vectors. It is only available for byte and bit vectors.
```json

{
  "query": {
    "script_score": {
      "query" : {
        "bool" : {
          "filter" : {
            "term" : {
              "status" : "published"
            }
          }
        }
      },
      "script": {
        "source": "(24 - hamming(params.queryVector, 'my_byte_dense_vector')) / 24", <1>
        "params": {
          "queryVector": [4, 3, 0]
        }
      }
    }
  }
}
```


#### L2 distance (Euclidean distance)

The `l2norm` function calculates L2 distance (Euclidean distance) between a given query vector and document vectors.
```json

{
  "query": {
    "script_score": {
      "query" : {
        "bool" : {
          "filter" : {
            "term" : {
              "status" : "published"
            }
          }
        }
      },
      "script": {
        "source": "1 / (1 + l2norm(params.queryVector, 'my_dense_vector'))",
        "params": {
          "queryVector": [4, 3.4, -0.2]
        }
      }
    }
  }
}
```


#### Checking for missing values

If a document doesn’t have a value for a vector field on which a vector function is executed, an error will be thrown.
You can check if a document has a value for the field `my_vector` with `doc['my_vector'].size() == 0`. Your overall script can look like this:
```js
"source": "doc['my_vector'].size() == 0 ? 0 : cosineSimilarity(params.queryVector, 'my_vector')"
```


#### Accessing vectors directly

You can access vector values directly through the following functions:
- `doc[<field>].vectorValue` – returns a vector’s value as an array of floats

<note>
  For `bit` vectors, it does return a `float[]`, where each element represents 8 bits.
</note>

- `doc[<field>].magnitude` – returns a vector’s magnitude as a float (for vectors created prior to version 7.5 the magnitude is not stored. So this function calculates it anew every time it is called).

<note>
  For `bit` vectors, this is just the square root of the sum of `1` bits.
</note>

For example, the script below implements a cosine similarity using these two functions:
```json

{
  "query": {
    "script_score": {
      "query" : {
        "bool" : {
          "filter" : {
            "term" : {
              "status" : "published"
            }
          }
        }
      },
      "script": {
        "source": """
          float[] v = doc['my_dense_vector'].vectorValue;
          float vm = doc['my_dense_vector'].magnitude;
          float dotProduct = 0;
          for (int i = 0; i < v.length; i++) {
            dotProduct += v[i] * params.queryVector[i];
          }
          return dotProduct / (vm * (float) params.queryVectorMag);
        """,
        "params": {
          "queryVector": [4, 3.4, -0.2],
          "queryVectorMag": 5.25357
        }
      }
    }
  }
}
```


#### Bit vectors and vector functions

When using `bit` vectors, not all the vector functions are available. The supported functions are:
- [`hamming`](#vector-functions-hamming) – calculates Hamming distance, the sum of the bitwise XOR of the two vectors
- [`l1norm`](#vector-functions-l1) – calculates L1 distance, this is simply the `hamming` distance
- [`l2norm`](#vector-functions-l2) - calculates L2 distance, this is the square root of the `hamming` distance
- [`dotProduct`](#vector-functions-dot-product) – calculates dot product. When comparing two `bit` vectors, this is the sum of the bitwise AND of the two vectors. If providing `float[]` or `byte[]`, who has `dims` number of elements, as a query vector, the `dotProduct` is the sum of the floating point values using the stored `bit` vector as a mask.

<note>
  When comparing `floats` and `bytes` with `bit` vectors, the `bit` vector is treated as a mask in big-endian order. For example, if the `bit` vector is `10100001` (e.g. the single byte value `161`) and its compared with array of values `[1, 2, 3, 4, 5, 6, 7, 8]` the `dotProduct` will be `1 + 3 + 8 = 16`.
</note>

Here is an example of using dot-product with bit vectors.
```json

{
  "mappings": {
    "properties": {
      "my_dense_vector": {
        "type": "dense_vector",
        "index": false,
        "element_type": "bit",
        "dims": 40 <1>
      }
    }
  }
}


{
  "my_dense_vector": [8, 5, -15, 1, -7] <2>
}


{
  "my_dense_vector": [-1, 115, -3, 4, -128]
}


{
  "my_dense_vector": [2, 18, -5, 0, -124]
}
```

```json

{
  "query": {
    "script_score": {
      "query" : {
        "match_all": {}
      },
      "script": {
        "source": "dotProduct(params.query_vector, 'my_dense_vector')",
        "params": {
          "query_vector": [8, 5, -15, 1, -7] <1>
        }
      }
    }
  }
}
```

```json

{
  "query": {
    "script_score": {
      "query" : {
        "match_all": {}
      },
      "script": {
        "source": "dotProduct(params.query_vector, 'my_dense_vector')",
        "params": {
          "query_vector": [0.23, 1.45, 3.67, 4.89, -0.56, 2.34, 3.21, 1.78, -2.45, 0.98, -0.12, 3.45, 4.56, 2.78, 1.23, 0.67, 3.89, 4.12, -2.34, 1.56, 0.78, 3.21, 4.12, 2.45, -1.67, 0.34, -3.45, 4.56, -2.78, 1.23, -0.67, 3.89, -4.34, 2.12, -1.56, 0.78, -3.21, 4.45, 2.12, 1.67] <1>
        }
      }
    }
  }
}
```

Currently, the `cosineSimilarity` function is not supported for `bit` vectors.

### Explain request

Using an [explain request](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-explain) 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:
```json

{
  "query": {
    "script_score": {
      "query": {
        "match": { "message": "elasticsearch" }
      },
      "script": {
        "source": """
          long count = doc['count'].value;
          double normalizedCount = count / 10;
          if (explanation != null) {
            explanation.set('normalized count = count / 10 = ' + count + ' / 10 = ' + normalizedCount);
          }
          return normalizedCount;
        """
      }
    }
  }
}
```

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