import os import random import json import shutil import faiss import numpy as np from tqdm import tqdm from FlagEmbedding import FlagLLMModel def create_index(embeddings: np.ndarray, use_gpu: bool = True): index = faiss.IndexFlatIP(len(embeddings[0])) embeddings = np.asarray(embeddings, dtype=np.float32) # if use_gpu: # co = faiss.GpuMultipleClonerOptions() # co.shard = True # co.useFloat16 = True # index = faiss.index_cpu_to_all_gpus(index, co=co) index.add(embeddings) return index if __name__ == '__main__': model = FlagLLMModel('/share/chaofan/models/bge-multilingual-gemma2', query_instruction_for_retrieval="Given a question, retrieve passages that answer the question.", query_instruction_format="{}\n{}", use_fp16=True) avaliable_languages = ['zh', 'en', 'ar', 'bn', 'es', 'fa', 'fi', 'fr', 'hi', 'id', 'ja', 'ko', 'ru', 'sw', 'te', 'th', 'de', 'yo'] new_dir = '/share/chaofan/code/bge_demo/data' new_emb_dir = '/share/chaofan/code/bge_demo/emb' for lang in tqdm(avaliable_languages[::-1], desc='language'): new_qrels_path = os.path.join(new_dir, lang, 'dev_qrels.jsonl') new_queries_path = os.path.join(new_dir, lang, 'dev_queries.jsonl') new_corpus_path = os.path.join(new_dir, lang, 'corpus.jsonl') os.makedirs(os.path.join(new_emb_dir, lang), exist_ok=True) if not os.path.exists(os.path.join(new_emb_dir, lang, 'corpus.npy')): data = [] with open(new_corpus_path) as f: for line in f: tmp = json.loads(line) data.append(tmp['title'] + ' ' + tmp['text']) doc_emb = model.encode_corpus(data, batch_size=256) np.save(os.path.join(new_emb_dir, lang, 'corpus.npy'), doc_emb) else: doc_emb = np.load(os.path.join(new_emb_dir, lang, 'corpus.npy')) faiss_index = create_index(doc_emb) faiss.write_index(faiss_index, os.path.join(new_emb_dir, lang, 'faiss.index'))