Refrain from investing in new vector database and instead expand the capabilities of existing ones

published on 2023/11/26
  • Accommodating vast amounts of data: Large-scale generative AI models require extensive data for training to capture intricate semantic and contextual information. Consequently, the volume of data has exploded. Vector databases, as adept data managers, play a crucial role in efficiently handling and managing such massive amounts of data.
  • Enabling accurate similarity searches and matching: Generated text from large-scale generative AI models often necessitates similarity searches and matching to provide precise replies, recommendations, or matching results. Traditional keyword-based search methods may fall short when it comes to complex semantics and context. Vector databases shine in this domain, offering high relevance and effectiveness for these tasks.
  • Supporting multimodal data processing: Large-scale generative AI models extend beyond text data and can handle multimodal data like images and speech. As comprehensive systems capable of storing and processing diverse data types, vector databases effectively support the storage, indexing, and querying of multimodal data, enhancing their versatility.