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pro vyhledávání: '"Bhati, Ishwar Singh"'
Embedding models can generate high-dimensional vectors whose similarity reflects semantic affinities. Thus, accurately and timely retrieving those vectors in a large collection that are similar to a given query has become a critical component of a wi
Externí odkaz:
http://arxiv.org/abs/2410.22347
Autor:
Aguerrebere, Cecilia, Hildebrand, Mark, Bhati, Ishwar Singh, Willke, Theodore, Tepper, Mariano
Retrieving the most similar vector embeddings to a given query among a massive collection of vectors has long been a key component of countless real-world applications. The recently introduced Retrieval-Augmented Generation is one of the most promine
Externí odkaz:
http://arxiv.org/abs/2402.02044
Modern deep learning models have the ability to generate high-dimensional vectors whose similarity reflects semantic resemblance. Thus, similarity search, i.e., the operation of retrieving those vectors in a large collection that are similar to a giv
Externí odkaz:
http://arxiv.org/abs/2312.16335
Akademický článek
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