input: Write a function to implement a fuzzy search algorithm in Python that can match strings with near similarities based on a given threshold for the Levenshtein distance. The function should return a list of strings from the choices list that match the query string within the specified similarity threshold.
  
  assert find_matches("apple", ["apples", "banana", "apple"], 1)==["apples", "apple"]
  assert find_matches("kitten", ["sitting", "kitchen", "kittens"], 3)==["sitting", "kitchen", "kittens"]
  assert find_matches("fuzz", ["fuzzy", "fun", "fuzy"], 2)==["fuzzy", "fuzy"]
  assert find_matches("tree", ["trees", "tee", "treat"], 1)==["trees"]
  assert find_matches("hello", ["world", "hello", "hell"], 0)==["hello"]
output: def find_matches(query, choices, threshold):
      from Levenshtein import distance
      return [choice for choice in choices if distance(query, choice) <= threshold]