anchor.diff:      default is set to `1.0` which corresponds to that only corrections with
       at most 1 misspelled term are returned.  Note that setting this too high
       can negatively impact performance. Low values like `1` or `2` are recommended
  -    otherwise the time spend in suggest calls might exceed the time spend in 
  +    otherwise the time spend in suggest calls might exceed the time spend in
       query execution.
   
  -`separator`:: 
  +`separator`::
       the separator that is used to separate terms in the
       bigram field. If not set the whitespace character is used as a
       separator.
   
  -`size`:: 
  +`size`::
       the number of candidates that are generated for each
       individual query term Low numbers like `3` or `5` typically produce good
       results. Raising this can bring up terms with higher edit distances. The
       default is `5`.
   
  -`analyzer`:: 
  +`analyzer`::
       Sets the analyzer to analyse to suggest text with.
       Defaults to the search analyzer of the suggest field passed via `field`.
   
  -`shard_size`:: 
  +`shard_size`::
       Sets the maximum number of suggested term to be
       retrieved from each individual shard. During the reduce phase, only the
       top N suggestions are returned based on the `size` option. Defaults to
       `5`.
   
  -`text`:: 
  +`text`::
       Sets the text / query to provide suggestions for.
   
   `highlight`::
  -    Sets up suggestion highlighting.  If not provided then 
  -    no `highlighted` field is returned.  If provided must 
  -    contain exactly `pre_tag` and `post_tag` which are 
  -    wrapped around the changed tokens.  If multiple tokens 
  -    in a row are changed the entire phrase of changed tokens 
  +    Sets up suggestion highlighting.  If not provided then
  +    no `highlighted` field is returned.  If provided must
  +    contain exactly `pre_tag` and `post_tag` which are
  +    wrapped around the changed tokens.  If multiple tokens
  +    in a row are changed the entire phrase of changed tokens
       is wrapped rather than each token.
   
   `collate`::
  
positive.code:     tokens passed to this candidate generator. This filter is applied to the
      original token before candidates are generated.
  
  `post_filter`:: 
      a filter (analyzer) that is applied to each of the
      generated tokens before they are passed to the actual phrase scorer.
  
  The following example shows a `phrase` suggest call with two generators,
  the first one is using a field containing ordinary indexed terms and the
  second one uses a field that uses terms indexed with a `reverse` filter 
  (tokens are index in reverse order). This is used to overcome the limitation 
  of the direct generators to require a constant prefix to provide 
  high-performance suggestions. The `pre_filter` and `post_filter` options 
  accept ordinary analyzer names.
  
  [source,js]
  
negative.code: 
  The `phrase` suggester supports multiple smoothing models to balance
  weight between infrequent grams (grams (shingles) are not existing in
  the index) and frequent grams (appear at least once in the index).
  
  [horizontal]
  `stupid_backoff`::
      a simple backoff model that backs off to lower
      order n-gram models if the higher order count is `0` and discounts the
      lower order n-gram model by a constant factor. The default `discount` is
      `0.4`. Stupid Backoff is the default model.
  
  `laplace`::
      a smoothing model that uses an additive smoothing where a
      constant (typically `1.0` or smaller) is added to all counts to balance
      weights, The default `alpha` is `0.5`.
  
  `linear_interpolation`::
      a smoothing model that takes the weighted
      mean of the unigrams, bigrams and trigrams based on user supplied
      weights (lambdas). Linear Interpolation doesn't have any default values.
  