[[query-dsl-script-score-query]] === Script Score Query 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 <>. 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 <> 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 [role="xpack"] [testenv="basic"] [[vector-functions]] ===== Functions for vector fields experimental[] These functions are used for for <> and <> 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. 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, an error will be thrown. You can check if a document has a value for the field `my_vector` by `doc['my_vector'].size() == 0`. Your overall script can look like this: [source,js] -------------------------------------------------- "source": "doc['my_vector'].size() == 0 ? 0 : cosineSimilarity(params.queryVector, doc['my_vector'])" -------------------------------------------------- // NOTCONSOLE NOTE: If a document's dense vector field has a number of dimensions different from the query's vector, an error will be thrown. [[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(, )`. 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 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 <>. ==== Transition from Function Score Query We are deprecating <>, 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]] ===== `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 <>. [[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` Script Score query has equivalent <> that can be used in script.