The Power of Vector Databases in the New Era of AI Search

In my 15 years as a software engineer, I've seen one truth hold constant: traditional databases are brilliant…...

Why AI Systems Use Vector Databases to Improve Performance and Reduce Costs

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Hermes: High-Performance Homomorphically Encrypted Vector Databases

Whitepaper called Hermes: High-Performance Homomorphically Encrypted Vector Databases...

Repository Vector Search Methods

The emergence of Large Language Models LLM has propelled Generative AI and surfaced one of its key components to a broad audience: Embeddings. Embeddings are a vector representation of data in a high-dimensional space capturing their semantic meaning. Vector representations allow for more efficie...

HoneyBee: Efficient Role-Based Access Control for Vector Databases Via Dynamic Partitioning

As vector databases gain traction in enterprise applications, robust access control has become critical to safeguard sensitive data. Access control in these systems is often implemented through hybrid vector queries, which combine nearest neighbor search on vector data with relational predicates...

Spring Tips: Vector Databases with Spring AI

Hi, Spring fans! In this installment, we look at the amazing support for vector databases in Spring AI...