最新的版本上有一些 error code about bert-embedding-frequently-asked-question HOT 2 OPEN

def search_annoy(self, owner_name, question, num=5):
    '''
    Author: xiaoyichao
    param {type}
    Description: 使用Annoy 召回
    '''
    sentences = read_vec2bin.read_bert_sents(owner_name=owner_name)
    annoy_index_path = os.path.join(
        dir_name, '../es/search_model/%s_annoy.index' % owner_name)
    **encodearrary = self.sentenceBERT.get_bert([question])**
    tc_index = AnnoyIndex(f=512, metric='angular')
    tc_index.load(annoy_index_path)
    items = tc_index.get_nns_by_vector(
        encodearrary[0], num, include_distances=True)
    sim_questions = [sentences[num_annoy] for num_annoy in items[0]]
    # sims = items[1]
    # index_nums = items[0]
    return sim_questions

def search_faiss(self, owner_name, question, num=5):
    '''
    Author: xiaoyichao
    param {type}
    Description: 使用Faiss 召回
    '''
    sentences = read_vec2bin.read_bert_sents(owner_name=owner_name)
    faiss_index_path = os.path.join(
        dir_name, '../es/search_model/%s_faiss.index' % owner_name)
    index = faiss.read_index(faiss_index_path)
    **question_vec = np.array(bc.encode([question])).astype('float32')**
    index.nprobe = 1
    sims, index_nums = index.search(question_vec, num)
    sim_questions = [sentences[num_faiss] for num_faiss in index_nums[0]]
    # index_nums = index_nums[0].tolist()
    # sims = sims[0].tolist()
    return sim_questions

好的，我周末看一下。感谢!

from bert-embedding-frequently-asked-question.

def search_annoy(self, owner_name, question, num=5):
    '''
    Author: xiaoyichao
    param {type}
    Description: 使用Annoy 召回
    '''
    sentences = read_vec2bin.read_bert_sents(owner_name=owner_name)
    annoy_index_path = os.path.join(
        dir_name, '../es/search_model/%s_annoy.index' % owner_name)
    **encodearrary = self.sentenceBERT.get_bert([question])**
    tc_index = AnnoyIndex(f=512, metric='angular')
    tc_index.load(annoy_index_path)
    items = tc_index.get_nns_by_vector(
        encodearrary[0], num, include_distances=True)
    sim_questions = [sentences[num_annoy] for num_annoy in items[0]]
    # sims = items[1]
    # index_nums = items[0]
    return sim_questions

def search_faiss(self, owner_name, question, num=5):
    '''
    Author: xiaoyichao
    param {type}
    Description: 使用Faiss 召回
    '''
    sentences = read_vec2bin.read_bert_sents(owner_name=owner_name)
    faiss_index_path = os.path.join(
        dir_name, '../es/search_model/%s_faiss.index' % owner_name)
    index = faiss.read_index(faiss_index_path)
    **question_vec = np.array(bc.encode([question])).astype('float32')**
    index.nprobe = 1
    sims, index_nums = index.search(question_vec, num)
    sim_questions = [sentences[num_faiss] for num_faiss in index_nums[0]]
    # index_nums = index_nums[0].tolist()
    # sims = sims[0].tolist()
    return sim_questions

已经修复，谢谢！

from bert-embedding-frequently-asked-question.