Last updated on February 21, 2026 by Editorial Team
Author(s): thomas reed
Originally published on Towards AI.
Numpy and/or SciKit-Learn can meet all your retrieval needs
Right now, vector databases are getting a lot of attention in the AI world, on the back of Retrieval Augmented Generation (RAG).
The article discusses the emerging popularity of vector databases in the AI landscape, specifically in the context of Retrieval Augmented Generation (RAG). It argues that although these tools are important for large-scale enterprise applications containing extensive vector data, smaller-scale implementations may not require the complexity of a dedicated vector database. Instead, NumPy and SciKit-Learn have been highlighted as viable options for building retrieval systems that can efficiently handle small data volumes, enabling fast search operations without the additional latency and cost of vector databases.
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Published via Towards AI
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