(index, tmpdir + "populated.index") if stage == 6: # perform a search from disk print("read " + tmpdir + "populated.index") index = faiss.read_index(tmpdir + "populated.index") index.nprobe = 16 # load query vectors and ground-truth xq = fvecs_read("sift1M/sift_query.fvecs"...
set_model_com_file_not_ready() def load_ict_checkpoint(only_query_model=False, only_block_model=False, no_grad=False, from_realm_chkpt=False): args = get_args() model = get_model(lambda: model_provider(only_query_model, only_block_model))25...
Load the index from the specified path if it is not None if not self.rebuild_index: if self.index_path and os.path.exists(self.index_path): self.load_index(self.index_path) else: os.remove(self.index_path) def load_index(self, index_path): """ Loads a Faiss index from a ...
mainly some folder name with just a number, then try to load that file path using faiss.load_local(). The path created after passing string path to Path(path) class is causing some issue. Expected behavior Issue: The actual file path is : D:\Question Answer Generative AI\Langchain\index...
from transformersimportBertModel,BertTokenizer # Load the pretrained model and tokenizer model=BertModel.from_pretrained('bert-base-uncased')tokenizer=BertTokenizer.from_pretrained('bert-base-uncased')# Load the text filewithopen('file.txt','r')asf:text=f.read()# Tokenize the text andgetthe te...
中秋快乐!本篇文章中,我们分别聊聊传统的文本检索与基于向量的文本检索,以及它们的实战。 在上一篇文章《聊聊来自元宇宙大厂 Meta 的相似度检索技术 Faiss》[1]中,我们有聊到如何快速入门向量检索技术,借助 Meta AI(Facebook Research)出品的 faiss 实现“最基础的文本内容相似度检索工具”,初步接触到了“语义检索”...
fromsentence_transformersimportSentenceTransformer model=SentenceTransformer('uer/sbert-base-chinese-nli')sentences=df['sentence'].tolist()sentence_embeddings=model.encode(sentences) 这个过程会比较久,消耗时间将会和你的电脑性能相关,我这边使用一台 Zen2 的普通笔记本,大概需要运行接近半个小时,所以这个时间不妨...
Source File: _faiss.py From mars with Apache License 2.0 7 votes def _load_index(ctx, op, index, device_id): return_index_type = _get_index_type(op.return_index_type, ctx) if return_index_type == 'object': # local return index elif return_index_type == 'filename': # local ...
# 需要导入模块: import faiss [as 别名]# 或者: from faiss importread_index[as 别名]def_load_index(ctx, op, index, device_id):return_index_type = _get_index_type(op.return_index_type, ctx)ifreturn_index_type =='object':# localreturnindexelifreturn_index_type =='filename':# local ...
@lru_cache(CACHED_VS_NUM) def load_vector_store(vs_path, embeddings): return MyFAISS.load_local(vs_path, embeddings) 可以看到最后返回的是一个vector_store的FAISS(VectorStore)类 @classmethoddef load_local( cls, folder_path: str, embeddings: Embeddings, index_name: str = "index") -> FAISS...