code: def test_only_1_positive_in_best_score_is_equivalent_to_normal_search():
      limit = 4
  
      # recommendation response
      reco_response = request_with_validation(
          api="/collections/{collection_name}/points/recommend",
          method="POST",
          path_params={"collection_name": collection_name},
          body={
              "positive": [1],
              "strategy": "best_score",
              "limit": limit,
              "exact": True,
          },
      )
      assert reco_response.ok
      assert len(reco_response.json()["result"]) == limit
  
      # Get vector from point 1
      vector = get_points([1])[0]["vector"]
  
      # Use normal search with that vector
      search_response = request_with_validation(
          api="/collections/{collection_name}/points/search",
          method="POST",
          path_params={"collection_name": collection_name},
          body={
              "vector": vector,
              "filter": {"must_not": [{"has_id": [1]}]},
              "limit": limit,
              "exact": True,
          },
      )
  
      assert search_response.ok
      assert len(search_response.json()["result"]) == limit
  
      # Scores can be different, but the ids and order should be the same
      reco_ids = [
          result["id"] for result in reco_response.json()["result"]
      ]
      search_ids = [
          result["id"] for result in search_response.json()["result"]
      ]
  
      assert reco_ids == search_ids
  
file_path: qdrant/openapi/tests/openapi_integration/test_recommend.py
imports: import pytest
  from .helpers.collection_setup import basic_collection_setup, drop_collection
  from .helpers.helpers import request_with_validation
label_desc: Use image node_id 6 for calling a global function with example usage: test_only_1_positive_in_best_score_is_equivalent_to_normal_search() without return types