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Description
Current:
>>> import rapidfuzz
>>> rapidfuzz.process.extract("aa", ["ab", "ba"], scorer=rapidfuzz.distance.Levenshtein.distance)
[('ab', 1, 0), ('ba', 1, 1)]
>>> rapidfuzz.process.extract("aa", ["ab", "ba", "cc"], scorer=rapidfuzz.distance.Levenshtein.distance)
[('ab', 1, 0), ('ba', 1, 1), ('cc', 2, 2)]
>>> rapidfuzz.process.extractOne("aa", ["ab", "ba", "cc"], scorer=rapidfuzz.distance.Levenshtein.distance)
('ab', 1, 0)
>>> rapidfuzz.process.extract("aa", ["ab", "ba", "cc"], scorer=rapidfuzz.distance.Levenshtein.distance, limit=1)
[('ab', 1, 0)]
Hypothetical/suggested:
>>> rapidfuzz.process.extract("aa", ["ab", "ba", "cc"], scorer=rapidfuzz.distance.Levenshtein.distance, limit="best")
[('ab', 1, 0), ('ba', 1, 1)] # returns all with the lowest score
Together with #188 seems to me that it would be a killer option 🥇 to find all closest matches for the bulk queries lists.
Thanks for the great work!
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