Generative AI with RDS as a Vector Store

Abstract

Generative Artificial Intelligence (AI) can be seen as a radically new paradigm in the study of data-driven applications, which enables machines to create content on their own in diverse fields, i.e., computer vision, scientific simulations, and natural language processing. A critical issue related to implementing the Generative AI systems is the practical storage, retrieval and control of high-dimensional vectors representations characteristic of the embedding-based AI systems. It is common that older databases cannot scale or provide fast similarity search, which is required in large scale collection of vector operations. The paper examines the concept of integrating Amazon Relational Database Service (RDS) as a vector store to the Generative AI systems, its practicability, performance, and its application to practice. We would propose a new design where the familiar RDS handles such structured relational data that high dimensional vectors can be both stored and read effectively. Our propositions that are founded on advanced indexing techniques, dimensionality reduction, and query optimization operate to indicate that the RDS can be an effective and consistent backend to Generative AI applications. The research paper entails a detailed experiment of text, image, and multimodal embeddings and compares the performance of retrieval, storage efficiency, and computational overhead with special purpose vector databases (such as faiiss and milvus). Conclusions RDS is capable of executing operations of a vector search with very low latency with the appropriate changes and adjustments and can offer a comparatively inexpensive alternative to organizations that already deploy the AWS services. Hybrid systems based on RDS and custom differentiated vector engines are also discussed in the paper to create the most preferable combination of consistency, scalability, and performance. The findings have valuable implications to both researchers and practitioners who may wish to implement Generative AI systems without necessarily incurring the overhead cost of the specialized infrastructure, further encouraging the practical application of AI-based solutions to the industry