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import os
import json
import pickle
import faiss
import numpy as np
import torch
from src.indicies.index_utils import convert_pkl_to_jsonl, get_passage_pos_ids
os.environ["TOKENIZERS_PARALLELISM"] = "true"
device = "cuda" if torch.cuda.is_available() else "cpu"
class FlatIndexer(object):
def __init__(
self,
index_path,
meta_file,
passage_dir=None,
pos_map_save_path=None,
):
self.index_path = index_path # path to store the final index
self.meta_file = meta_file # path to save the index id to db id map
self.passage_dir = passage_dir
self.pos_map_save_path = pos_map_save_path
self.cuda = False
if os.path.exists(index_path) and os.path.exists(self.meta_file):
print("Loading index...")
self.index = faiss.read_index(index_path)
self.index_id_to_db_id = self.load_index_id_to_db_id()
else:
raise NotImplementedError
if self.pos_map_save_path is not None:
self.psg_pos_id_map = self.load_psg_pos_id_map()
def load_index_id_to_db_id(
self,
):
with open(self.meta_file, "rb") as reader:
index_id_to_db_id = pickle.load(reader)
return index_id_to_db_id
def build_passage_pos_id_map(
self,
):
convert_pkl_to_jsonl(self.passage_dir)
passage_pos_ids = get_passage_pos_ids(self.passage_dir, self.pos_map_save_path)
return passage_pos_ids
def load_psg_pos_id_map(
self,
):
if os.path.exists(self.pos_map_save_path):
with open(self.pos_map_save_path, "rb") as f:
psg_pos_id_map = pickle.load(f)
else:
psg_pos_id_map = self.build_passage_pos_id_map()
return psg_pos_id_map
def _id2psg(self, shard_id, chunk_id):
filename, position = self.psg_pos_id_map[shard_id][chunk_id]
with open(filename, "r") as file:
file.seek(position)
line = file.readline()
return json.loads(line)
def _get_passage(self, index_id):
try:
shard_id, chunk_id = self.index_id_to_db_id[index_id]
except:
shard_id, chunk_id = 0, self.index_id_to_db_id[index_id]
return self._id2psg(shard_id, chunk_id)
def get_retrieved_passages(self, all_indices):
passages, db_ids = [], []
for query_indices in all_indices:
passages_per_query = [
self._get_passage(int(index_id))["text"] for index_id in query_indices
]
db_ids_per_query = [
self.index_id_to_db_id[int(index_id)] for index_id in query_indices
]
passages.append(passages_per_query)
db_ids.append(db_ids_per_query)
return passages, db_ids
def search(self, query_embs, k=4096):
all_scores, all_indices = self.index.search(query_embs.astype(np.float32), k)
all_passages, db_ids = self.get_retrieved_passages(all_indices)
return all_scores.tolist(), all_passages, db_ids
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