'Returns related documents from the model as a list of (similarity, document)-tuples.
The given words can be a string (one word), a list or tuple of words, or a Document.'

def vector_space_search(self, words=[], **kwargs):

    top = kwargs.pop('top', 10)
    if (not isinstance(words, Document)):
        kwargs.setdefault('filter', (lambda w: w))
        kwargs.setdefault('stopwords', True)
        words = Document(words)
    if (len([w for w in words if (w in self.vector)]) == 0):
        return []
    (m, words._model) = (words._model, self)
    (n, words._model) = (self.nearest_neighbors(words, top), m)
    words._model = m
    return n
