PostgreSQL with the pgvector extension allows tables to be used as storage for vectors, each of which is saved as a row. It also allows any number of metadata columns to be added. In an enterprise ...

Vector databases are all the rage, judging by the number of startups entering the space and the investors ponying up for a piece of the pie. The proliferation of large language models (LLMs ...

MariaDB has recently released MariaDB Community Server 11.8 as generally available, its yearly long-term support (LTS) ...

Generative AI is revolutionizing data and analytics, but its applications demand advanced data management capabilities to handle vast, diverse, and complex datasets that include images, video ...

Vector databases are at the heart of this, because that’s how data created by AI modelling is stored and from where it is accessed during AI inference.. In this article, we look at vector ...

About two years ago, popular cache database Redis was among a wave of vendors that added vector search capabilities to their platforms, driven by the surge of interest in generative AI.… ...

Today, this vector-based approach has evolved into sophisticated vector databases, systems that mirror how our own brains process and retrieve information. This convergence of human cognition and ...

Did you know that over 80% of the data generated today is unstructured? Traditional databases often fall short in managing this type of data efficiently. That’s where vector databases come into ...

Concluding Words. RAG and vector databases represent a major advancement in AI technology, providing more accurate and contextually relevant responses.

Vector databases are all the rage, judging by the number of startups entering the space and the investors ponying up for a piece of the pie. The proliferation of large language models (LLMs) and ...