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#
# Pyserini: Reproducible IR research with sparse and dense representations
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import argparse
import copy
import json
import os
import shutil
import time
import faiss
import nmslib
from scipy.sparse import csr_matrix
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--input', type=str, help='path to embeddings directory', required=True)
parser.add_argument('--output', type=str, help='path to output index dir', required=True)
parser.add_argument('--M', type=int, default=256, required=False)
parser.add_argument('--efC', type=int, default=256, required=False)
parser.add_argument('--threads', type=int, default=12, required=False)
args = parser.parse_args()
if not os.path.exists(args.output):
os.mkdir(args.output)
is_sparse = False
if 'index' in os.listdir(args.input):
shutil.copy(os.path.join(args.input, 'docid'), os.path.join(args.output, 'docid'))
bf_index = faiss.read_index(os.path.join(args.input, 'index'))
vectors = bf_index.reconstruct_n(0, bf_index.ntotal)
else:
vectors = []
for filename in os.listdir(args.input):
path = os.path.join(args.input, filename)
with open(path) as f_in, open(os.path.join(args.output, 'docid'), 'w') as f_out:
for line in f_in:
info = json.loads(line)
docid = info['id']
vector = info['vector']
f_out.write(f'{docid}\n')
vectors.append(vector)
tokens = set()
if isinstance(vectors[0], dict):
is_sparse = True
for vec in vectors:
for key in vec:
tokens.add(key)
token2id = {}
with open(os.path.join(args.output, 'tokens'), 'w') as f:
for idx, tok in enumerate(tokens):
token2id[tok] = idx
f.write(f'{tok}\n')
if is_sparse:
matrix_row, matrix_col, matrix_data = [], [], []
for i, vec in enumerate(vectors):
weight_dict = vec
tokens = weight_dict.keys()
col = [token2id[tok] for tok in tokens]
data = weight_dict.values()
matrix_row.extend([i] * len(weight_dict))
matrix_col.extend(col)
matrix_data.extend(data)
vectors = csr_matrix((matrix_data, (matrix_row, matrix_col)), shape=(len(vectors), len(token2id)))
M = args.M
efC = args.efC
num_threads = args.threads
index_time_params = {'M': M, 'indexThreadQty': num_threads, 'efConstruction': efC, 'post': 0}
if is_sparse:
index = nmslib.init(method='hnsw', space='negdotprod_sparse', data_type=nmslib.DataType.SPARSE_VECTOR)
else:
index = nmslib.init(method='hnsw', space='negdotprod', data_type=nmslib.DataType.DENSE_VECTOR)
index.addDataPointBatch(vectors)
start = time.time()
index.createIndex(index_time_params, print_progress=True)
end = time.time()
index_time = end - start
print('Index-time parameters', index_time_params)
print('Indexing time = %f' % index_time)
index.saveIndex(os.path.join(args.output, 'index.bin'), save_data=True)
metadata = copy.deepcopy(index_time_params)
metadata['index-time'] = index_time
metadata['type'] = 'sparse' if is_sparse else 'dense'
json.dump(metadata, open(os.path.join(args.output, 'meta'), 'w'), indent=4)
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