Cutting-Edge Challenges and Solutions for the Integration of Vector Database and AI Technology

Zhongqi Zhu

doi:10.71222/w5sk9106

Authors

Zhongqi Zhu Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY, 11201, USA Author

DOI:

https://doi.org/10.71222/w5sk9106

Keywords:

vector database, semantic modeling, intelligent retrieval, system integration

Abstract

Vector databases, based on semantic queries and high-dimensional vectors, have become an important supporting environment for artificial intelligence applications such as intelligent question answering, multimodal fusion, and knowledge extraction. In the in-depth integration and application of artificial intelligence technology, it has been found that low indexing efficiency, semantic matching errors, and insufficient system adaptability in vector databases can affect collaboration efficiency and intelligence effectiveness. To ensure efficient execution and stable operation of intelligent applications, improvements are needed in retrieval structure, semantic processing mechanism, and system interfaces. This article provides an overview of the main technical issues around typical application backgrounds, forming targeted solutions to promote the steady development of the integration of vector databases and artificial intelligence technology towards standardization and high quality.

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