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#![allow(clippy::inline_always)]
// -----------------------------------------------------------------------------
impl<K: std::hash::Hash + Ord> crate::simple::search_index::SearchIndex<K> {
/// Scans the entire search index for the closest matching _n_ keywords
/// using the provided keyword and configured string similarity metric. This
/// feature relies on the [strsim](https://crates.io/crates/strsim)
/// crate.
///
/// When the user's keyword that is meant to be autocompleted returns no
/// matches, this can be used to find the best matches for substitution.
///
/// All keywords in the search index will potentially be examined.
///
/// # Input
///
/// * `user_keyword` ยท This keyword is used to search the search index.
///
/// For example, if the user provided the misspelled word `nthing`, this
/// could potentially return `nothing` as an alternative keyword if it
/// was present in the index.
///
/// # Output
///
/// This method returns an iterator over the top _n_ autocompletion options.
///
/// Each item the returned iterator is comprised of a keyword, and the
/// records associated with each keyword.
///
/// The number of autocompletion options are defined by the
/// `maximum_autocomplete_options` option in the search index.
///
/// If no keywords or reasonable matches are found, this method will return
/// an empty iterator.
///
/// # Notes
///
/// * This method differs from `strsim_keyword` in that this is a generic
/// method. This method will be monomorphized for each `strsim` string
/// similarity metric (`DamerauLevenshtein`, `Jaro`, `Osa`, etc.)
///
/// `strsim_keyword` will call these monomorphized methods using
/// dynamic-dispatch, based on the search index's string similarity metric
/// settings.
#[inline(always)]
pub(crate) fn strsim_keyword_metric<'s, M>(
&'s self,
user_keyword: &str,
index_range: &str,
top_scores: &mut crate::simple::internal::fuzzers::FuzzyTopScores::<'s, K, f64>,
)
where M: crate::simple::internal::fuzzers::strsim::Metric {
// Scan the search index for the highest scoring keywords:
self.b_tree_map
// Get matching keywords starting with (partial) keyword string:
.range(kstring::KString::from_ref(index_range)..)
// We did not specify an end bound for our `range` function (see
// above.) `range` will return _every_ keyword greater than the
// supplied keyword. The below `take_while` will effectively break
// iteration when we reach a keyword that does not start with our
// supplied (partial) keyword.
.take_while(|(index_keyword, _keys)|
index_keyword.starts_with::<&str>(index_range.as_ref())
)
// For example: if the user typed "gold", and the autocompletion
// options were "gold" and "golden", this filter would remove "gold"
// as an autocompletion option because it's currently what's already
// typed.
//
// This is commented-out because returning "gold" as an option
// provides feedback to the user that their keyword exists in
// the index.
/* .filter(|(index_keyword, _index_keys)|
index_keyword.as_str() != user_keyword
) */
// For each keyword in the search index:
.for_each(|(index_keyword, index_keys)| {
// Using this keyword from the search index, calculate its
// similarity to the user's keyword:
let score = M::similarity(index_keyword, user_keyword);
// Insert the score into the top scores (if it's normal and high
// enough):
if score.is_normal() && score >= self.fuzzy_minimum_score {
top_scores.insert(index_keyword, index_keys, score);
} // if
}); // for_each
} // fn
} // impl