def control_filter(self, control_dicts, plans, top_k):
    """
    Filters control items based on their similarity to a set of keywords.
    :param control_dicts: The dictionary of control items to be filtered.
    :param plans: The list of plans to be used for filtering.
    :param top_k: The number of top control items to return.
    :return: The filtered control items.
    """
    scores_items = []
    filtered_control_dict = {}

    for label, control_item in control_dicts.items():
        control_text = control_item.element_info.name.lower()
        score = self.control_filter_score(control_text, plans)
        scores_items.append((label, score))
    topk_scores_items = heapq.nlargest(top_k, (scores_items), key=lambda x: x[1])
    topk_items = [
        (score_item[0], score_item[1]) for score_item in topk_scores_items
    ]

    for label, control_item in control_dicts.items():
        if label in topk_items:
            filtered_control_dict[label] = control_item
    return filtered_control_dict

def control_filter_score(self, control_text, plans):
    """
    Calculates the score for a control item based on the similarity between its text and a set of keywords.
    :param control_text: The text of the control item.
    :param plans: The plan to be used for calculating the similarity.
    :return: The score (0-1) indicating the similarity between the control text and the keywords.
    """

    plan_embedding = self.get_embedding(plans)
    control_text_embedding = self.get_embedding(control_text)
    return max(self.cos_sim(control_text_embedding, plan_embedding).tolist()[0])