Without a vector database, managing and retrieving these vectors would be as cumbersome as finding a specific book in the world’s largest library, drastically slowing down the AI's performance.

Using vector databases and search brings with it a few common generative AI risks such as data quality, modeling issues, and more. New issues include hallucinations and confabulations .

Milvus, a Linux Foundation AI and data project, for example, is a well-known vector database of choice among enterprises that’s easy to try out because of its vibrant open source development.

With Redis Data Integration (RDI) in the Redis 7.2 update, the company is providing an integrated approach to help get data from other data sources including Oracle Database, PostgreSQL, MySQL and ...

Since the turn of the year, we’ve also seen Index Ventures lead a $9.5 million seed round into Superlinked, a platform that transforms complex data into vector embeddings.

The vector data abstractions library, introduced October 29, provides library authors and developers with the ability to perform create-read-update-delete (CRUD) ...

Vector stores are the water of generative AI, agentic AI and systems that include retrieval-augmented generation (RAG), so building them now will give businesses the foundation they need for the ...

Vast Data Inc. today announced enhancements to its Vast Data Platform, enabling it to support structured and unstructured data in a single DataSpace with linear and secure scaling.The enhancement ...

“With Vector Search, Tigris Data gives developers the ability to deliver fast, accurate, and personalized recommendations to their users,” said Tariq in a release. “This powerful tool is designed to ...

A Vector DB stores and manages unstructured data — text, images, audio, etc. — as vector embeddings (numerical format). These embeddings capture the semantic relationships between the data points.