High-performant Vector Database for Artificial Intelligence (AI) Applications
Machine Learning (ML) and Deep Learning (DL) have been the primary growth driver of Artificial Intelligence (AI) and has seen widespread adoption in areas such as Computer Vision, Speech Processing, Natural Language Processing, and Graph Search, among many others. It is also well-known that AI both needs and produces large amounts of data. However, traditional data repositories have not scaled effectively to handle the large amounts of vector representations that are common in AI applications - in such cases, searching for similarities across high-dimensional vectors is inefficient. To address such limitations, vector databases have been developed to address the limitations of traditional hash-based searches and search scalability, enabling similarity searches across large datasets.
This technology offer is a unified Online Analytical Processing (OLAP) data platform that supports approximate vector search, enabling efficient searching over billion-scale structured data and vector data. The data engine simplifies the process of building enterprise-level AI applications such as search and recommendation systems, video analytics, text-based searches, and chatbots while accelerating the development of production-ready systems. Developers no longer need to deal with complicated scripts to query vector data as low latency, high-performance structured data, and vector data searches are made possible via vector data indexing methods and the use of extended Structured Query Language (SQL) syntax.