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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 json
import os
from elasticsearch import Elasticsearch
from elasticsearch.helpers import streaming_bulk
from tqdm import tqdm
INDEX_NAME = "msmarco-v1-passage"
MSMARCO_PATH = 'data/msmarco-passage-unicoil/'
def create_pseudo_doc(vector):
results = []
for key in vector:
results += [key] * vector[key]
pesudo_doc = " ".join(results)
return pesudo_doc
def generate_unicoil_vector():
for file in os.listdir(MSMARCO_PATH):
path = os.path.join(MSMARCO_PATH, file)
with open(path) as f:
for line in f:
info = json.loads(line)
docid = info['id']
text = create_pseudo_doc(info['vector'])
action = {
"_op_type": "update",
"_index": INDEX_NAME,
"_id": docid,
"doc": {"vector": text},
}
yield action
client = Elasticsearch("http://localhost:9200")
print("Indexing documents...")
successes = 0
for ok, action in tqdm(streaming_bulk(
client=client, index=INDEX_NAME, actions=generate_unicoil_vector(),
)):
successes += ok
print(f"Indexed {successes} documents") |