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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.
#
from elasticsearch import Elasticsearch
from pyserini.search import get_topics, QueryEncoder
from tqdm import tqdm
client = Elasticsearch("http://localhost:9200", timeout=1000)
topics = get_topics("msmarco-passage-dev-subset")
query_encoder = QueryEncoder.load_encoded_queries('sbert-msmarco-passage-dev-subset')
with open('run.es_sbert.tsv', 'w') as f:
for qid in tqdm(topics):
query = topics[qid]['title']
formated_query = {
"field": "text-vector",
"query_vector": query_encoder.encode(query),
"k": 10,
"num_candidates": 100
}
resp = client.knn_search(index="tct-colbert-hnsw", knn=formated_query)
for i in range(len(resp["hits"]["hits"])):
pid = resp["hits"]["hits"][i]['_id']
record = f"{qid}\t{pid}\t{i+1}\n"
f.write(record)
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