A fast implementation of BM25 in Python.
BM25 is a simple and fast ranking function for search engines operating on words (tokens).
It does not play well with misspelling so use it only in contexts where that's not a problem.

The base BM25 implementation is from dorianbrown/rank_bm25.

Initialize BM25 by passing it a corpus, aka an iterator over tokenized documents (a list of Strings).

from fast_bm25 import BM25

# Load your corpus
corpus = ...

bm25 = new BM25(corpus)
results = bm25.get_top_n(["largest", "city", "in", "Japan"], corpus);

It's not a python package, copy the file if you want to use it

In a text corpus, the most common words (the, a, an, ...) are often the least informative.
By cutting them off from the query and only searching documents containing at least a word of the query, BM25 gain a lot of speed while loosing very little precision.
This trade-off is controlled by the parameter alpha: higher alpha => more speed and more word cut-off.
At $\alpha = -\inf$ the algorithm is equivalent to regular BM25.