anchor.diff:  
   `max_inspections`::
       A factor that is used to multiply with the
  -    `shards_size` in order to inspect more candidate spell corrections on
  +    `shards_size` in order to inspect more candidate spelling corrections on
       the shard level. Can improve accuracy at the cost of performance.
       Defaults to 5.
   
  
positive.code: 
  [horizontal]
  `lowercase_terms`::
      Lower cases the suggest text terms after text analysis.
  
  `max_edits`::
      The maximum edit distance candidate suggestions can
      have in order to be considered as a suggestion. Can only be a value
      between 1 and 2. Any other value result in an bad request error being
      thrown. Defaults to 2.
  
  `prefix_length`::
      The number of minimal prefix characters that must
      match in order be a candidate suggestions. Defaults to 1. Increasing
      this number improves spellcheck performance. Usually misspellings don't
      occur in the beginning of terms. (Old name "prefix_len" is deprecated)
  
  
negative.code:     suggestion should appear in. This can be specified as an absolute number
      or as a relative percentage of number of documents. This can improve
      quality by only suggesting high frequency terms. Defaults to 0f and is
      not enabled. If a value higher than 1 is specified, then the number
      cannot be fractional. The shard level document frequencies are used for
      this option.
  
  `max_term_freq`::
      The maximum threshold in number of documents in which a
      suggest text token can exist in order to be included. Can be a relative
      percentage number (e.g., 0.4) or an absolute number to represent document
      frequencies. If a value higher than 1 is specified, then fractional can
      not be specified. Defaults to 0.01f. This can be used to exclude high
      frequency terms -- which are usually spelled correctly -- from being spellchecked.
      This also improves the spellcheck performance. The shard level document frequencies
      are used for this option.
  
  `string_distance`::
      Which string distance implementation to use for comparing how similar
      suggested terms are. Five possible values can be specified:
      
      ** `internal`: The default based on damerau_levenshtein but highly optimized
      for comparing string distance for terms inside the index.
      ** `damerau_levenshtein`: String distance algorithm based on
      Damerau-Levenshtein algorithm.
      ** `levenshtein`: String distance algorithm based on Levenshtein edit distance
      algorithm.
      ** `jaro_winkler`: String distance algorithm based on Jaro-Winkler algorithm.
      ** `ngram`: String distance algorithm based on character n-grams.
  