Building a QA semantic search engine in 3 minutes


The complete example can be found

As the first example, we will implement a simple QA search engine using bert-as-service in just three minutes. No kidding! The goal is to find similar questions to user’s input and return the corresponding answer. To start, we need a list of question-answer pairs. Fortunately, this README file already contains a list of FAQ, so I will just use that to make this example perfectly self-contained. Let’s first load all questions and show some statistics.

prefix_q = '##### **Q:** '
with open('') as fp:
    questions = [v.replace(prefix_q, '').strip() for v in fp if v.strip() and v.startswith(prefix_q)]
    print('%d questions loaded, avg. len of %d' % (len(questions), np.mean([len(d.split()) for d in questions])))

This gives 33 questions loaded, avg. len of 9. So looks like we have enough questions. Now start a BertServer with uncased_L-12_H-768_A-12 pretrained BERT model:

bert-serving-start -num_worker=1 -model_dir=/data/cips/data/lab/data/model/uncased_L-12_H-768_A-12

Next, we need to encode our questions into vectors:

bc = BertClient(port=5555, port_out=5556)
doc_vecs = bc.encode(questions)

Finally, we are ready to receive new query and perform a simple “fuzzy” search against the existing questions. To do that, every time a new query is coming, we encode it as a vector and compute its dot product with doc_vecs; sort the result descendingly; and return the top-k similar questions as follows:

while True:
    query = input('your question: ')
    query_vec = bc.encode([query])[0]
    # compute normalized dot product as score
    score = np.sum(query_vec * doc_vecs, axis=1) / np.linalg.norm(doc_vecs, axis=1)
    topk_idx = np.argsort(score)[::-1][:topk]
    for idx in topk_idx:
        print('> %s\t%s' % (score[idx], questions[idx]))

That’s it! Now run the code and type your query, see how this search engine handles fuzzy match: