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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 cmd
import json
import os
import random
from pyserini.search.lucene import LuceneSearcher
from pyserini.search.faiss import FaissSearcher, TctColBertQueryEncoder, AnceQueryEncoder
from pyserini.search.hybrid import HybridSearcher
from pyserini import search
class MsMarcoDemo(cmd.Cmd):
dev_topics = list(search.get_topics('msmarco-passage-dev-subset').values())
ssearcher = LuceneSearcher.from_prebuilt_index('msmarco-passage')
dsearcher = None
hsearcher = None
searcher = ssearcher
k = 10
prompt = '>>> '
# https://stackoverflow.com/questions/35213134/command-prefixes-in-python-cli-using-cmd-in-pythons-standard-library
def precmd(self, line):
if line[0] == '/':
line = line[1:]
return line
def do_help(self, arg):
print(f'/help : returns this message')
print(f'/k [NUM] : sets k (number of hits to return) to [NUM]')
print(f'/model [MODEL] : sets encoder to use the model [MODEL] (one of tct, ance)')
print(f'/mode [MODE] : sets retriever type to [MODE] (one of sparse, dense, hybrid)')
print(f'/random : returns results for a random question from dev subset')
def do_k(self, arg):
print(f'setting k = {int(arg)}')
self.k = int(arg)
def do_mode(self, arg):
if arg == "sparse":
self.searcher = self.ssearcher
elif arg == "dense":
if self.dsearcher is None:
print(f'Specify model through /model before using dense retrieval.')
return
self.searcher = self.dsearcher
elif arg == "hybrid":
if self.hsearcher is None:
print(f'Specify model through /model before using hybrid retrieval.')
return
self.searcher = self.hsearcher
else:
print(
f'Mode "{arg}" is invalid. Mode should be one of [sparse, dense, hybrid].')
return
print(f'setting retriver = {arg}')
def do_model(self, arg):
if arg == "tct":
encoder = TctColBertQueryEncoder("castorini/tct_colbert-msmarco")
index = "msmarco-passage-tct_colbert-hnsw"
elif arg == "ance":
encoder = AnceQueryEncoder("castorini/ance-msmarco-passage")
index = "msmarco-passage-ance-bf"
else:
print(
f'Model "{arg}" is invalid. Model should be one of [tct, ance].')
return
self.dsearcher = FaissSearcher.from_prebuilt_index(
index,
encoder
)
self.hsearcher = HybridSearcher(self.dsearcher, self.ssearcher)
print(f'setting model = {arg}')
def do_random(self, arg):
q = random.choice(self.dev_topics)['title']
print(f'question: {q}')
self.default(q)
def do_EOF(self, line):
return True
def default(self, q):
hits = self.searcher.search(q, self.k)
for i in range(0, len(hits)):
raw_doc = None
if isinstance(self.searcher, LuceneSearcher):
raw_doc = hits[i].raw
else:
doc = self.searcher.doc(hits[i].docid)
if doc:
raw_doc = doc.raw()
jsondoc = json.loads(raw_doc)
print(f'{i + 1:2} {hits[i].score:.5f} {jsondoc["contents"]}')
if __name__ == '__main__':
MsMarcoDemo().cmdloop()
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