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import os
import faiss
import json
import numpy as np
from FlagEmbedding import FlagLLMModel, FlagAutoModel
def create_index(embeddings: np.ndarray):
index = faiss.IndexFlatIP(len(embeddings[0]))
embeddings = np.asarray(embeddings, dtype=np.float32)
index.add(embeddings)
return index
def move_index_to_gpu(index):
try:
co = faiss.GpuMultipleClonerOptions()
co.shard = True
co.useFloat16 = True
index = faiss.index_cpu_to_all_gpus(index, co=co)
except:
print('not support faiss-gpu')
return index
def load_model_util(previous_model, model_path):
self_model_path = '/share/chaofan/models/bge-multilingual-gemma2'
if model_path == 'BAAI/bge-multilingual-gemma2':
if previous_model is not None and previous_model.model_name_or_path == self_model_path:
return previous_model
model = FlagLLMModel(self_model_path,
query_instruction_for_retrieval="Given a question, retrieve Wikipedia passages that answer the question.",
query_instruction_format="<instruct>{}\n<query>{}",
use_fp16=True,
devices=['cuda:0'])
else:
if previous_model is not None and previous_model.model_name_or_path == model_path:
return previous_model
model = FlagAutoModel.from_finetuned(model_path,
use_fp16=True,
devices=['cuda:0'])
if previous_model is not None:
del previous_model
model.model.half()
model.model = model.model.to('cuda:0')
return model
def load_corpus_util(base_dir, lang):
corpus_path = os.path.join(base_dir, lang, 'corpus.jsonl')
data = []
with open(corpus_path) as f:
for line in f:
tmp = json.loads(line)
data.append(tmp)
queries = []
queries_path = os.path.join(base_dir, lang, 'dev_queries.jsonl')
with open(queries_path) as f:
for line in f:
tmp = json.loads(line)
queries.append(tmp['text'])
if len(queries) >= 5:
break
return data, queries
def build_index_util(emb_dir, lang, model, data):
emb_path = os.path.join(emb_dir, lang, 'corpus.npy')
index_path = os.path.join(emb_dir, lang, 'faiss.index')
if os.path.exists(index_path):
faiss_index = faiss.read_index(index_path)
return move_index_to_gpu(faiss_index)
if os.path.exists(emb_path):
doc_emb = np.load(emb_path)
else:
doc_emb = model.encode_corpus(data, batch_size=256)
np.save(emb_path, doc_emb)
faiss_index = create_index(doc_emb)
# faiss.write_index(faiss_index, index_path)
faiss_index = move_index_to_gpu(faiss_index)
return faiss_index
def search_util(model, query, corpus, faiss_index, topk):
query_emb = model.encode_queries(query)
query_emb = query_emb.reshape(1, -1)
scores, inxs = faiss_index.search(query_emb, k=topk)
data = []
for idx in inxs[0]:
data.append(corpus[idx])
return scores[0], data |