Function score query

The script_score function allows you to wrap another query and customize the scoring of it optionally with a computation derived from other numeric field values in the doc using a script expression. Here is a simple sample:

response = client.search(
  body: {
    query: {
      function_score: {
        query: {
          match: {
            message: 'elasticsearch'
          }
        },
        script_score: {
          script: {
            source: "Math.log(2 + doc['my-int'].value)"
          }
        }
      }
    }
  }
)
puts response

GET /_search
{
  "query": {
    "function_score": {
      "query": {
        "match": { "message": "elasticsearch" }
      },
      "script_score": {
        "script": {
          "source": "Math.log(2 + doc['my-int'].value)"
        }
      }
    }
  }
}

On top of the different scripting field values and expression, the _score script parameter can be used to retrieve the score based on the wrapped query.

Scripts compilation is cached for faster execution. If the script has parameters that it needs to take into account, it is preferable to reuse the same script, and provide parameters to it:

response = client.search(
  body: {
    query: {
      function_score: {
        query: {
          match: {
            message: 'elasticsearch'
          }
        },
        script_score: {
          script: {
            params: {
              a: 5,
              b: 1.2
            },
            source: "params.a / Math.pow(params.b, doc['my-int'].value)"
          }
        }
      }
    }
  }
)
puts response

GET /_search
{
  "query": {
    "function_score": {
      "query": {
        "match": { "message": "elasticsearch" }
      },
      "script_score": {
        "script": {
          "params": {
            "a": 5,
            "b": 1.2
          },
          "source": "params.a / Math.pow(params.b, doc['my-int'].value)"
        }
      }
    }
  }
}

Note that unlike the custom_score query, the score of the query is multiplied with the result of the script scoring. If you wish to inhibit this, set "boost_mode": "replace"

The weight score allows you to multiply the score by the provided weight. This can sometimes be desired since boost value set on specific queries gets normalized, while for this score function it does not. The number value is of type float.

"weight" : number

The random_score generates scores that are uniformly distributed from 0 up to but not including 1. By default, it uses the internal Lucene doc ids as a source of randomness, which is very efficient but unfortunately not reproducible since documents might be renumbered by merges.

In case you want scores to be reproducible, it is possible to provide a seed and field. The final score will then be computed based on this seed, the minimum value of field for the considered document and a salt that is computed based on the index name and shard id so that documents that have the same value but are stored in different indexes get different scores. Note that documents that are within the same shard and have the same value for field will however get the same score, so it is usually desirable to use a field that has unique values for all documents. 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.

response = client.search(
  body: {
    query: {
      function_score: {
        random_score: {
          seed: 10,
          field: '_seq_no'
        }
      }
    }
  }
)
puts response

res, err := es.Search(
	es.Search.WithBody(strings.NewReader(`{
	  "query": {
	    "function_score": {
	      "random_score": {
	        "seed": 10,
	        "field": "_seq_no"
	      }
	    }
	  }
	}`)),
	es.Search.WithPretty(),
)
fmt.Println(res, err)

GET /_search
{
  "query": {
    "function_score": {
      "random_score": {
        "seed": 10,
        "field": "_seq_no"
      }
    }
  }
}

The field_value_factor function allows you to use a field from a document to influence the score. It’s similar to using the script_score function, however, it avoids the overhead of scripting. If used on a multi-valued field, only the first value of the field is used in calculations.

As an example, imagine you have a document indexed with a numeric my-int field and wish to influence the score of a document with this field, an example doing so would look like:

response = client.search(
  body: {
    query: {
      function_score: {
        field_value_factor: {
          field: 'my-int',
          factor: 1.2,
          modifier: 'sqrt',
          missing: 1
        }
      }
    }
  }
)
puts response

GET /_search
{
  "query": {
    "function_score": {
      "field_value_factor": {
        "field": "my-int",
        "factor": 1.2,
        "modifier": "sqrt",
        "missing": 1
      }
    }
  }
}

Which will translate into the following formula for scoring:

sqrt(1.2 * doc['my-int'].value)

There are a number of options for the field_value_factor function:

Decay functions score a document with a function that decays depending on the distance of a numeric field value of the document from a user given origin. This is similar to a range query, but with smooth edges instead of boxes.

To use distance scoring on a query that has numerical fields, the user has to define an origin and a scale for each field. The origin is needed to define the “central point” from which the distance is calculated, and the scale to define the rate of decay. The decay function is specified as

"DECAY_FUNCTION": { 
    "FIELD_NAME": { 
          "origin": "11, 12",
          "scale": "2km",
          "offset": "0km",
          "decay": 0.33
    }
}

In the above example, the field is a geo_point and origin can be provided in geo format. scale and offset must be given with a unit in this case. If your field is a date field, you can set scale and offset as days, weeks, and so on. Example:

response = client.search(
  body: {
    query: {
      function_score: {
        gauss: {
          "@timestamp": {
            origin: '2013-09-17',
            scale: '10d',
            offset: '5d',
            decay: 0.5
          }
        }
      }
    }
  }
)
puts response

GET /_search
{
  "query": {
    "function_score": {
      "gauss": {
        "@timestamp": {
          "origin": "2013-09-17", 
          "scale": "10d",
          "offset": "5d",         
          "decay": 0.5            
        }
      }
    }
  }
}

In the first example, your documents might represents hotels and contain a geo location field. You want to compute a decay function depending on how far the hotel is from a given location. You might not immediately see what scale to choose for the gauss function, but you can say something like: "At a distance of 2km from the desired location, the score should be reduced to one third." The parameter "scale" will then be adjusted automatically to assure that the score function computes a score of 0.33 for hotels that are 2km away from the desired location.

In the second example, documents with a field value between 2013-09-12 and 2013-09-22 would get a weight of 1.0 and documents which are 15 days from that date a weight of 0.5.

    "DECAY_FUNCTION": {
        "FIELD_NAME": {
              "origin": ...,
              "scale": ...
        },
        "multi_value_mode": "avg"
    }

Suppose you are searching for a hotel in a certain town. Your budget is limited. Also, you would like the hotel to be close to the town center, so the farther the hotel is from the desired location the less likely you are to check in.

You would like the query results that match your criterion (for example, "hotel, Nancy, non-smoker") to be scored with respect to distance to the town center and also the price.

Intuitively, you would like to define the town center as the origin and maybe you are willing to walk 2km to the town center from the hotel.
In this case your origin for the location field is the town center and the scale is ~2km.

If your budget is low, you would probably prefer something cheap above something expensive. For the price field, the origin would be 0 Euros and the scale depends on how much you are willing to pay, for example 20 Euros.

In this example, the fields might be called "price" for the price of the hotel and "location" for the coordinates of this hotel.

The function for price in this case would be

"gauss": { 
    "price": {
          "origin": "0",
          "scale": "20"
    }
}

and for location:

"gauss": { 
    "location": {
          "origin": "11, 12",
          "scale": "2km"
    }
}

Suppose you want to multiply these two functions on the original score, the request would look like this:

response = client.search(
  body: {
    query: {
      function_score: {
        functions: [
          {
            gauss: {
              price: {
                origin: '0',
                scale: '20'
              }
            }
          },
          {
            gauss: {
              location: {
                origin: '11, 12',
                scale: '2km'
              }
            }
          }
        ],
        query: {
          match: {
            properties: 'balcony'
          }
        },
        score_mode: 'multiply'
      }
    }
  }
)
puts response

res, err := es.Search(
	es.Search.WithBody(strings.NewReader(`{
	  "query": {
	    "function_score": {
	      "functions": [
	        {
	          "gauss": {
	            "price": {
	              "origin": "0",
	              "scale": "20"
	            }
	          }
	        },
	        {
	          "gauss": {
	            "location": {
	              "origin": "11, 12",
	              "scale": "2km"
	            }
	          }
	        }
	      ],
	      "query": {
	        "match": {
	          "properties": "balcony"
	        }
	      },
	      "score_mode": "multiply"
	    }
	  }
	}`)),
	es.Search.WithPretty(),
)
fmt.Println(res, err)

GET /_search
{
  "query": {
    "function_score": {
      "functions": [
        {
          "gauss": {
            "price": {
              "origin": "0",
              "scale": "20"
            }
          }
        },
        {
          "gauss": {
            "location": {
              "origin": "11, 12",
              "scale": "2km"
            }
          }
        }
      ],
      "query": {
        "match": {
          "properties": "balcony"
        }
      },
      "score_mode": "multiply"
    }
  }
}

Next, we show how the computed score looks like for each of the three possible decay functions.

Normal decay, keyword gauss

edit

When choosing gauss as the decay function in the above example, the contour and surface plot of the multiplier looks like this:

Suppose your original search results matches three hotels :

• "Backback Nap"
• "Drink n Drive"
• "BnB Bellevue".

"Drink n Drive" is pretty far from your defined location (nearly 2 km) and is not too cheap (about 13 Euros) so it gets a low factor a factor of 0.56. "BnB Bellevue" and "Backback Nap" are both pretty close to the defined location but "BnB Bellevue" is cheaper, so it gets a multiplier of 0.86 whereas "Backpack Nap" gets a value of 0.66.

Exponential decay, keyword exp

edit

When choosing exp as the decay function in the above example, the contour and surface plot of the multiplier looks like this:

Linear decay, keyword linear

edit

When choosing linear as the decay function in the above example, the contour and surface plot of the multiplier looks like this:

Only numeric, date, and geopoint fields are supported.

If the numeric field is missing in the document, the function will return 1.