Billion-Scale Vector Search: Architecture, Algorithms, and Enterprise  Deployment

Hemang Manish Shah

Billion-Scale Vector Search: Architecture, Algorithms, and Enterprise Deployment

Authors

Hemang Manish Shah

Keywords:

Vector Similarity Search, Approximate Nearest Neighbor, Semantic Embeddings, Product Quantization, Hybrid Retrieval

Abstract

The exponential growth of high-dimensional embedding corpora in production artificial intelligenceinfrastructure has elevated vector similarity search from a tractable in-memory problem into a complex, multi-disciplinary systems engineering challenge.

References

Kelvin Guu et al., "Retrieval Augmented Language Model Pre-Training," Proceedings of Machine Learning Research, 2020. [Online]. Available: https://proceedings.mlr.press/v119/guu20a.html

Stephen Robertson and Hugo Zaragoza, "The Probabilistic Relevance Framework: BM25 and Beyond," Foundations and Trends in Information Retrieval, 2009. [Online]. Available: https://dl.acm.org/doi/10.1561/1500000019

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Published

2026-05-19

How to Cite

Hemang Manish Shah. (2026). Billion-Scale Vector Search: Architecture, Algorithms, and Enterprise Deployment . Journal of Computational Analysis and Applications (JoCAAA), 35(5), 221–228. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5477