Similarity module

A similarity (scoring / ranking model) defines how matching documents are scored. Similarity is per field, meaning that via the mapping one can define a different similarity per field.

Configuring a custom similarity is considered an expert feature and the builtin similarities are most likely sufficient as is described in similarity.

Most existing or custom Similarities have configuration options which can be configured via the index settings as shown below. The index options can be provided when creating an index or updating index settings.

PUT /index
{
    "settings" : {
        "index" : {
            "similarity" : {
              "my_similarity" : {
                "type" : "DFR",
                "basic_model" : "g",
                "after_effect" : "l",
                "normalization" : "h2",
                "normalization.h2.c" : "3.0"
              }
            }
        }
    }
}

Here we configure the DFRSimilarity so it can be referenced as my_similarity in mappings as is illustrate in the below example:

PUT /index/_mapping/_doc
{
  "properties" : {
    "title" : { "type" : "text", "similarity" : "my_similarity" }
  }
}

TF/IDF based similarity that has built-in tf normalization and is supposed to work better for short fields (like names). See Okapi_BM25 for more details. This similarity has the following options:

Type name: BM25

The classic similarity that is based on the TF/IDF model. This similarity has the following option:

Type name: classic

Similarity that implements the divergence from randomness framework. This similarity has the following options:

All options but the first option need a normalization value.

Type name: DFR

Similarity that implements the divergence from independence model. This similarity has the following options:

Type name: DFI

Information based model . The algorithm is based on the concept that the information content in any symbolic distribution sequence is primarily determined by the repetitive usage of its basic elements. For written texts this challenge would correspond to comparing the writing styles of different authors. This similarity has the following options:

Type name: IB

LM Dirichlet similarity . This similarity has the following options:

Type name: LMDirichlet

LM Jelinek Mercer similarity . The algorithm attempts to capture important patterns in the text, while leaving out noise. This similarity has the following options:

Type name: LMJelinekMercer

A similarity that allows you to use a script in order to specify how scores should be computed. For instance, the below example shows how to reimplement TF-IDF:

PUT /index
{
  "settings": {
    "number_of_shards": 1,
    "similarity": {
      "scripted_tfidf": {
        "type": "scripted",
        "script": {
          "source": "double tf = Math.sqrt(doc.freq); double idf = Math.log((field.docCount+1.0)/(term.docFreq+1.0)) + 1.0; double norm = 1/Math.sqrt(doc.length); return query.boost * tf * idf * norm;"
        }
      }
    }
  },
  "mappings": {
    "_doc": {
      "properties": {
        "field": {
          "type": "text",
          "similarity": "scripted_tfidf"
        }
      }
    }
  }
}

PUT /index/_doc/1
{
  "field": "foo bar foo"
}

PUT /index/_doc/2
{
  "field": "bar baz"
}

POST /index/_refresh

GET /index/_search?explain=true
{
  "query": {
    "query_string": {
      "query": "foo^1.7",
      "default_field": "field"
    }
  }
}

Which yields:

{
  "took": 12,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 1,
    "max_score": 1.9508477,
    "hits": [
      {
        "_shard": "[index][0]",
        "_node": "OzrdjxNtQGaqs4DmioFw9A",
        "_index": "index",
        "_type": "_doc",
        "_id": "1",
        "_score": 1.9508477,
        "_source": {
          "field": "foo bar foo"
        },
        "_explanation": {
          "value": 1.9508477,
          "description": "weight(field:foo in 0) [PerFieldSimilarity], result of:",
          "details": [
            {
              "value": 1.9508477,
              "description": "score from ScriptedSimilarity(weightScript=[null], script=[Script{type=inline, lang='painless', idOrCode='double tf = Math.sqrt(doc.freq); double idf = Math.log((field.docCount+1.0)/(term.docFreq+1.0)) + 1.0; double norm = 1/Math.sqrt(doc.length); return query.boost * tf * idf * norm;', options={}, params={}}]) computed from:",
              "details": [
                {
                  "value": 1.0,
                  "description": "weight",
                  "details": []
                },
                {
                  "value": 1.7,
                  "description": "query.boost",
                  "details": []
                },
                {
                  "value": 2.0,
                  "description": "field.docCount",
                  "details": []
                },
                {
                  "value": 4.0,
                  "description": "field.sumDocFreq",
                  "details": []
                },
                {
                  "value": 5.0,
                  "description": "field.sumTotalTermFreq",
                  "details": []
                },
                {
                  "value": 1.0,
                  "description": "term.docFreq",
                  "details": []
                },
                {
                  "value": 2.0,
                  "description": "term.totalTermFreq",
                  "details": []
                },
                {
                  "value": 2.0,
                  "description": "doc.freq",
                  "details": []
                },
                {
                  "value": 3.0,
                  "description": "doc.length",
                  "details": []
                }
              ]
            }
          ]
        }
      }
    ]
  }
}

You might have noticed that a significant part of the script depends on statistics that are the same for every document. It is possible to make the above slightly more efficient by providing an weight_script which will compute the document-independent part of the score and will be available under the weight variable. When no weight_script is provided, weight is equal to 1. The weight_script has access to the same variables as the script except doc since it is supposed to compute a document-independent contribution to the score.

The below configuration will give the same tf-idf scores but is slightly more efficient:

PUT /index
{
  "settings": {
    "number_of_shards": 1,
    "similarity": {
      "scripted_tfidf": {
        "type": "scripted",
        "weight_script": {
          "source": "double idf = Math.log((field.docCount+1.0)/(term.docFreq+1.0)) + 1.0; return query.boost * idf;"
        },
        "script": {
          "source": "double tf = Math.sqrt(doc.freq); double norm = 1/Math.sqrt(doc.length); return weight * tf * norm;"
        }
      }
    }
  },
  "mappings": {
    "_doc": {
      "properties": {
        "field": {
          "type": "text",
          "similarity": "scripted_tfidf"
        }
      }
    }
  }
}

Type name: scripted

By default, Elasticsearch will use whatever similarity is configured as default.

You can change the default similarity for all fields in an index when it is created:

PUT /index
{
  "settings": {
    "index": {
      "similarity": {
        "default": {
          "type": "classic"
        }
      }
    }
  }
}

If you want to change the default similarity after creating the index you must close your index, send the following request and open it again afterwards:

POST /index/_close

PUT /index/_settings
{
  "index": {
    "similarity": {
      "default": {
        "type": "classic"
      }
    }
  }
}

POST /index/_open