def setUp(self):
# create indexer
file_dir_path = os.path.dirname(__file__)
data_dir = 'data_samples/ms_marco_passage_retrieval'
self.abs_data_dir = os.path.abspath(
os.path.join(file_dir_path, *([os.pardir] * 4), data_dir))
self.index_name = "final"
indexer_config = {
"batch_size": 5,
"fields": ["doc_id", "content", "pack_info"],
"indexer": {
"name": "ElasticSearchIndexer",
"hparams": {
"index_name": self.index_name,
"hosts": "localhost:9200",
"algorithm": "bm25"
},
"other_kwargs": {
"request_timeout": 10,
"refresh": True
}
}
}
self.indexer = ElasticSearchIndexer(
config={"index_name": self.index_name})
nlp: Pipeline[DataPack] = Pipeline()
nlp.set_reader(MSMarcoPassageReader())
nlp.add(DataSelectorIndexProcessor(), config=indexer_config)
nlp.initialize()
self.size = 0
for _ in nlp.process_dataset(self.abs_data_dir):
self.size += 1
self.test_dir = tempfile.mkdtemp()
def setUp(self):
self.reader = MSMarcoPassageReader()
self.data_dir = 'data_samples/ms_marco_passage_retrieval'
corpus_file = os.path.join(self.data_dir, 'collection.tsv')
self.expected_content = {}
with open(corpus_file, 'r') as f:
for line in f.readlines():
key, value = tuple(line.split('\t', 1))
self.expected_content[key] = value
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--config_file",
default="./config.yml",
help="Config YAML filepath")
args = parser.parse_args()
# loading config
config = yaml.safe_load(open(args.config_file, "r"))
file_dir_path = os.path.dirname(__file__)
data_dir = 'data_samples/ms_marco_passage_retrieval'
abs_data_dir = os.path.abspath(
os.path.join(file_dir_path, *([os.pardir] * 3), data_dir))
reader = MSMarcoPassageReader()
nlp = CreateIndexerPipeline(reader=reader,
reader_config=None,
indexer_config=config["indexer_config"])
nlp.create_index(abs_data_dir)
def setUp(self):
file_dir_path = os.path.dirname(__file__)
data_dir = 'data_samples/ms_marco_passage_retrieval'
self.abs_data_dir = os.path.abspath(
os.path.join(file_dir_path, *([os.pardir] * 4), data_dir))
corpus_file = os.path.join(self.abs_data_dir, 'collection.tsv')
self.expected_content = set()
with open(corpus_file, 'r') as f:
for line in f.readlines():
key, value = tuple(line.split('\t', 1))
self.expected_content.add(value)
self.index_name = "test_indexer"
indexer_config = {
"batch_size": 5,
"fields": ["doc_id", "content", "pack_info"],
"indexer": {
"name": "ElasticSearchIndexer",
"hparams": {
"index_name": self.index_name,
"hosts": "localhost:9200",
"algorithm": "bm25"
},
"other_kwargs": {
"request_timeout": 10,
"refresh": True
}
}
}
self.indexer = ElasticSearchIndexer(
config={"index_name": self.index_name})
self.nlp: Pipeline[DataPack] = Pipeline()
self.reader = MSMarcoPassageReader()
self.processor = DataSelectorIndexProcessor()
self.nlp.set_reader(self.reader)
self.nlp.add(self.processor, config=indexer_config)
self.nlp.initialize()
def setUp(self):
self.pipeline = Pipeline()
self.pipeline.set_reader(MSMarcoPassageReader())
self.pipeline.initialize()
root_path = os.path.abspath(
os.path.join(
os.path.dirname(os.path.abspath(__file__)),
os.pardir,
os.pardir,
os.pardir,
os.pardir,
))
self.data_dir = os.path.join(
root_path, "data_samples/ms_marco_passage_retrieval")
corpus_file = os.path.join(self.data_dir, "collection.tsv")
self.expected_content = {}
with open(corpus_file, "r") as f:
for line in f.readlines():
key, value = tuple(line.split("\t", 1))
self.expected_content[key] = value
from forte.common.configuration import Config
from forte.data.data_pack import DataPack
from forte.data.readers import MSMarcoPassageReader
from forte.pipeline import Pipeline
from forte.processors.ir import ElasticSearchTextIndexProcessor
logging.basicConfig(level=logging.INFO)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config_file",
default="./config.yml",
help="Config YAML filepath")
args = parser.parse_args()
config = yaml.safe_load(open(args.config_file, "r"))
config = Config(config, default_hparams=None)
nlp: Pipeline[DataPack] = Pipeline()
nlp.set_reader(MSMarcoPassageReader())
nlp.add(ElasticSearchTextIndexProcessor(), config=config.create_index)
nlp.initialize()
data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
config.data.relative_path)
for idx, pack in enumerate(nlp.process_dataset(data_path)):
if idx + 1 > 0 and (idx + 1) % 10000 == 0:
print(f"Indexed {idx + 1} packs")