anchor.diff:  
   [horizontal]
   `lowercase_terms`::
  -    Lower cases the suggest text terms after text analysis.
  +    Lowercases 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
  +    between 1 and 2. Any other value results in a 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
  +    match in order be a candidate for 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)
   
  
positive.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 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 an 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 from being spellchecked. High frequency terms are
      usually spelled correctly on top of 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.
  
negative.code: 
  `field`::
      The field to fetch the candidate suggestions from. This is
      a required option that either needs to be set globally or per
      suggestion.
  
  `analyzer`::
  