input: Write a function to generate a fuzzy search algorithm in Python that can match strings with near similarities using the Levenshtein distance as a metric. The function should return a list of strings from the provided list that are close enough to the query string based on a given threshold.
  assert find_matches("apple", ["appel", "aple", "aplle", "orange"], 2)==["appel", "aple", "aplle"]
  assert find_matches("python", ["pyhton", "pyton", "pytyhon", "java"], 1)==["pyhton", "pyton", "pytyhon"]
  assert find_matches("openai", ["opneai", "openaai", "openai", "closedai"], 2)==["opneai", "openaai", "openai"]
  assert find_matches("example", [], 3)==[]
  assert find_matches("test", ["test", "tset", "est", "tes"], 0)==["test"]
output: def find_matches(query, string_list, threshold):
      from Levenshtein import distance
      return [s for s in string_list if distance(query, s) <= threshold]