input: Write a function to generate a fuzzy search algorithm in Python that can match strings with near similarities. The function should take a query string, a list of choices, and a threshold value for the maximum Levenshtein distance allowed for a match. The function should return a list of strings from the choices that match the query within the specified distance threshold.
  
  def find_matches(query, choices, threshold):
      # Convert query and choices to lower case
      query, choices = query.lower(), [choice.lower() for choice in choices]
      # Initialize empty list to store matches
      matches = []
      
      # Loop through each choice
      for choice in choices:
          # Calculate Levenshtein distance between query and choice
          distance = levenshtein_distance(query, choice)
          # If distance is less than threshold, append choice to matches
          if distance <= threshold:
              matches.append(choice)
      
      return matches
  
  assert find_matches("apple", ["apples", "appel", "aple", "banana"], 2) == ["apples", "appel", "aple"]
  assert find_matches("dog", ["dogs", "doggy", "cat", "doog"], 1) == ["dogs", "doog"]
  assert find_matches("red", ["read", "reed", "bread"], 2) == ["read", "reed"]
  assert find_matches("xyz", ["abc", "def", "ghi"], 0) == []
  assert find_matches("", ["a", "b", "c"], 1) == []
output: def find_matches(query, choices, threshold):
      query = query.lower()
      choices = [choice.lower() for choice in choices]
      matches = []
      
      for choice in choices:
          distance = levenshtein_distance(query, choice)
          if distance <= threshold:
              matches.append(choice)
      
      return matches