TL;DR: KIOXIA's AiSAQ technology, combined with NVIDIA's cuVS Library, enables efficient scaling of high-dimensional vector searches to 4.8 billion vectors on a single server, achieving up to 20X ...

What's the role of vector databases in the agentic AI world? That's a question that organizations have been coming to terms with in recent months. The narrative had real momentum. As large language ...

Japanese regulators are investigating Microsoft Corp.’s local unit over possible anti-competitive practices involving the Azure cloud platform. Japan’s Fair Trade Commission is probing whether the ...

Abstract: Vector set search, an underexplored similarity search paradigm, aims to find vector sets similar to a query set. This search paradigm leverages the inherent structural alignment between sets ...

If you have ever searched for a home online, you know the routine. Set a price range. Click a few filters. Run the search. Start over. Again and again. Now imagine skipping all of that and simply ...

Alibaba Tongyi Lab research team released ‘Zvec’, an open source, in-process vector database that targets edge and on-device retrieval workloads. It is positioned as ‘the SQLite of vector databases’ ...

Python still holds the top ranking in the monthly Tiobe index of programming language popularity, leading by more than 10 percentage points over second-place C. But Python’s popularity actually has ...

In this quickstart, you use agentic retrieval to create a conversational search experience powered by documents indexed in Azure AI Search and a large language model (LLM) from Azure OpenAI in Foundry ...

Microsoft used Ignite 2025 to push Azure Cosmos DB further into AI search and agentic workflows, highlighting new capabilities aimed at developers building retrieval-heavy applications and multi-agent ...

The second major cloud outage in less than two weeks, Azure’s downtime highlights the “brittleness” of a digital ecosystem that depends on a few companies never making mistakes. Microsoft's problems ...

Most vector search systems struggle with a basic problem: how to break complex documents into searchable pieces. The typical approach is to split text into fixed size chunks of 200 to 500 tokens, this ...