Numpy and/or SciKit-Learn could meet all your recovery needs
Currently, thanks to retrieval augmented generation (RAG), vector databases are attracting a lot of attention in the AI world.
The article discusses the growing popularity of vector databases in the AI landscape, particularly in the context of retrieval augmented generation (RAG). It argues that while these tools are crucial for large-scale enterprise applications with lots of vector data, smaller-scale implementations might not require the complexity of a dedicated vector database. Instead, NumPy and SciKit-Learn are presented as viable alternatives for creating retrieval systems capable of efficiently handling smaller volumes of data, enabling rapid search operations without the latency and added costs of vector databases.
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