Full disclosure I don't have any experience using the BM25 ranking, however I do have quite a bit of experience with gensim's TF-IDF and LSI distributed models, along with gensim's similarity index.

The author does a really good job at keeping a readable codebase, so if you're ever having trouble with anything like this again, I recommend just jumping into the source code.

Looking at the source code: https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/summarization/bm25.py

So I initialized a BM25() object with the documents you pasted above.

It looks like our good old friend Radim didn't include a function to calculate the average_idf for us, which no biggie, we can just plagarize line 65 for our cause:

average_idf = sum(map(lambda k: float(bm25.idf[k]), bm25.idf.keys())) / len(bm25.idf.keys())

Then, well if I understand the original intention of get_scores correctly, you should get each BM25 score with respect to your original query, simply by doing

scores = bm_25_object.get_scores(query_doc, average_idf)

Which returns all the scores for each document, and then, if I understand the BM25 ranking based on what I read on this wikipedia page: https://en.wikipedia.org/wiki/Okapi_BM25

You should be able to just pick the document with the highest score as follows:

best_result = docs[scores.index(max(scores))]

So the first document should be the most relevant to your query? I hope that's what you were expecting anyways, and I hope that this helped in some capacity. Good luck!