How to use the text2vec.algorithm.rank_bm25.BM25Okapi function in text2vec

query = "windy London"
tokenized_query = query.split(" ")

doc_scores = bm25.get_scores(tokenized_query)
print(doc_scores)

a = bm25.get_top_n(tokenized_query, corpus, n=2)
print(a)

print("*" * 45)

corpus = ['女网红能火的只是一小部分', '当下最火的男明星为鹿晗', "How is the weather today?"]
tokenized_corpus = [segment(doc) for doc in corpus]

bm25 = BM25Okapi(tokenized_corpus)

query = '当下最火的女网红是谁？'
tokenized_query = segment(query)

doc_scores = bm25.get_scores(tokenized_query)
print(doc_scores)

a = bm25.get_top_n(tokenized_query, corpus, n=2)
print(a)

def init(self):
        if not self.bm25_instance:
            if not self.corpus:
                logger.error('corpus is none, set corpus with docs.')
                raise ValueError("must set corpus, which is documents, list of str")

            if isinstance(self.corpus, str):
                self.corpus = [self.corpus]

            self.corpus_seg = {k: self.tokenizer.tokenize(k) for k in self.corpus}
            self.bm25_instance = BM25Okapi(corpus=list(self.corpus_seg.values()))