generate.py 文件源码

def search_docs(inputs, max_ex=5, opts=None):
    """Given a set of document ids (returned by ranking for a question), search
    for top N best matching (by heuristic) paragraphs that contain the answer.
    """
    if not opts:
        raise RuntimeError('Options dict must be supplied.')

    doc_ids, q_tokens, answer = inputs
    examples = []
    for i, doc_id in enumerate(doc_ids):
        for j, paragraph in enumerate(re.split(r'\n+', fetch_text(doc_id))):
            found = find_answer(paragraph, q_tokens, answer, opts)
            if found:
                # Reverse ranking, giving priority to early docs + paragraphs
                score = (found[0], -i, -j, random.random())
                if len(examples) < max_ex:
                    heapq.heappush(examples, (score, found[1]))
                else:
                    heapq.heappushpop(examples, (score, found[1]))
    return [e[1] for e in examples]