Elastic 9.3.0 is now available, featuring enhanced vector search indexing for RAG applications and significant upgrades to ...

Abstract: Retrieval-augmented generation pipelines store large volumes of embedding vectors in vector databases for semantic search. In Compute Express Link (CXL)-based tiered memory systems, ...

Gemini Embedding 2 ships cross-modality retrieval with Matryoshka vectors, offering flexible dimensions for cost and accuracy tradeoffs.

Qdrant's $50M Series B and version 1.17 release make the case that agentic AI didn't simplify vector search — it scaled the ...

The 13% succeeding with AI platforms share one move: unifying their data on an extensible Postgres® foundation.

Google has launched Gemini Embedding 2, its first natively multimodal embedding model supporting text, images, video, audio, ...

Google Gemini Embedding 2 unifies text, images, audio, PDFs, and video; it supports 3,072-dimension vectors, simplifying retrieval stacks.

In this tutorial, we build an elastic vector database simulator that mirrors how modern RAG systems shard embeddings across distributed storage nodes. We implement consistent hashing with virtual ...

Abstract: Retrieval-augmented Large Models (RALMs) have emerged as a promising paradigm to enhance large language models (LLMs) by integrating external knowledge. However, the inherent complexity of ...

VittoriaDB is a high-performance, embedded vector database designed for local AI development and production deployments. Built with simplicity and performance in mind, it provides a zero-configuration ...

Endee.io launches Endee, an open source vector database delivering fast, accurate, and cost-efficient AI and semantic search at scale. Endee rethinks vector DBs for high recall, low latency, and low ...