Elasticsearch allows you to configure a scoring algorithm or similarity per field. The similarity setting provides a simple way of choosing a similarity algorithm other than the default BM25, such as TF/IDF.

Similarities are mostly useful for text fields, but can also apply to other field types.

Custom similarities can be configured by tuning the parameters of the built-in similarities. For more details about this expert options, see the similarity module.

The only similarities which can be used out of the box, without any further configuration are:

The Okapi BM25 algorithm. The algorithm used by default in Elasticsearch and Lucene. See Pluggable Similarity Algorithms for more information.
The TF/IDF algorithm which used to be the default in Elasticsearch and Lucene. See Lucene’s Practical Scoring Function for more information.
A simple boolean similarity, which is used when full-text ranking is not needed and the score should only be based on whether the query terms match or not. Boolean similarity gives terms a score equal to their query boost.

The similarity can be set on the field level when a field is first created, as follows:

PUT my_index
  "mappings": {
    "_doc": {
      "properties": {
        "default_field": { 
          "type": "text"
        "classic_field": {
          "type": "text",
          "similarity": "classic" 
        "boolean_sim_field": {
          "type": "text",
          "similarity": "boolean" 

The default_field uses the BM25 similarity.

The classic_field uses the classic similarity (ie TF/IDF).

The boolean_sim_field uses the boolean similarity.