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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 gzip | |
import json | |
import os | |
import time | |
from pyserini.index.lucene import LuceneIndexer, JacksonObjectMapper | |
if __name__ == '__main__': | |
parser = argparse.ArgumentParser(description='Index MS MARCO Passage corpus.') | |
parser.add_argument('--input', required=True, type=str, help='Path to MS MARCO Passage corpus.') | |
parser.add_argument('--index', required=True, type=str, help='Path to index.') | |
parser.add_argument('--threads', required=True, type=int, help='Number of threads.') | |
parser.add_argument('--batch-size', required=True, type=int, help='Batch size.') | |
parser.add_argument('--raw', action='store_true', default=False, help="Directly index raw documents.") | |
parser.add_argument('--dict', action='store_true', default=False, help="Parse and index Python dictionaries.") | |
args = parser.parse_args() | |
mapper = JacksonObjectMapper() | |
start = time.time() | |
print(f'input: {args.input}') | |
print(f'index: {args.index}') | |
print(f'threads: {args.threads}') | |
print(f'batch size: {args.batch_size}') | |
print(f'index raw? {args.raw}') | |
batch = [] | |
indexer = LuceneIndexer(args.index, threads=args.threads) | |
cnt = 0 | |
batch_cnt = 0 | |
for file in os.listdir(args.input): | |
if not file.endswith('gz'): | |
continue | |
with gzip.open(os.path.join(args.input, file), 'r') as f: | |
for line in f: | |
if args.raw: | |
batch.append(line.decode()) | |
elif args.dict: | |
obj = json.loads(line.decode()) | |
batch.append({'id': obj['id'], 'contents': obj['contents']}) | |
else: | |
obj = json.loads(line.decode()) | |
batch.append(mapper.createObjectNode().put('id', obj['id']).put('contents', obj['contents'])) | |
cnt += 1 | |
if len(batch) == args.batch_size: | |
if args.raw: | |
indexer.add_batch_raw(batch) | |
elif args.dict: | |
indexer.add_batch_dict(batch) | |
else: | |
indexer.add_batch_json(batch) | |
batch = [] | |
batch_cnt += 1 | |
if cnt % 100000 == 0: | |
cur = time.time() | |
print(f'{cnt} docs indexed, {batch_cnt} batches, {cnt/(cur - start):.0f} docs/s') | |
# Remember to add the final batch. | |
if args.raw: | |
indexer.add_batch_raw(batch) | |
elif args.dict: | |
indexer.add_batch_dict(batch) | |
else: | |
indexer.add_batch_json(batch) | |
indexer.close() | |
end = time.time() | |
print(f'Total {cnt} docs indexed in {end - start:.0f}s, {cnt/(end - start):.0f} docs/s') | |