Abstract: Vector databases typically manage large collections of embedding vectors. As AI applications are growing rapidly, the number of embeddings that need to be stored and indexed is increasing.

Vector search underpins most retrieval-augmented generation (RAG) pipelines. At scale, it gets expensive. Storing 10 million document embeddings in float32 consumes 31 GB of RAM. For dev teams running ...

Git isn't hard to learn, and when you combine Git and GitHub, you've just made the learning process significantly easier. This two-hour Git and GitHub video tutorial shows you how to get started with ...

In this tutorial, we build an EverMem-style persistent agent OS. We combine short-term conversational context (STM) with long-term vector memory using FAISS so the agent can recall relevant past ...

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

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ABSTRACT: As the integration of Large Language Models (LLMs) into scientific R&D accelerates, the associated privacy risks become increasingly critical. Scientific NoSQL repositories, which often ...

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