| 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_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 |
