"""
Retrieve top k candidate standard terms for normalization using oaklib.
"""

# import argparse

from oaklib import get_adapter
from oaklib.datamodels.search import SearchConfiguration

adapter = get_adapter("ols:")


def get_candidates(term: str, top_k: int = 10) -> list[str]:
  """
  Get top k candidates for RAG.
  """
  # Set config for search (limit # terms returned)
  cfg = SearchConfiguration(limit=top_k)

  results = adapter.basic_search(term, config=cfg)
  labels = list(adapter.labels(results))  # list of tuples of ids and labels

  # print(f"## Query: {term} -> {labels}")
  candidates = list(label for _, label in labels)
  return candidates


# def main():
#   parser = argparse.ArgumentParser(
#     description="Fetch top-K candidate passages for a given term (RAG)"
#   )
#   parser.add_argument(
#     "term", type=str, help="The query term or prompt for which to retrieve candidates"
#   )
#   parser.add_argument(
#     "-k",
#     "--top_k",
#     type=int,
#     default=10,
#     help="Number of top candidates to return (default: 10)",
#   )

#   args = parser.parse_args()

#   # Call your function
#   candidates = get_candidates(args.term)

#   print(f"\nTerm: {args.term!r}")
#   print(f"Top {args.top_k} candidates:")
#   for i, cand in enumerate(candidates, start=1):
#     print(f"  {i:2d}. {cand}")


# if __name__ == "__main__":
#   main()