Why MongoDB thinks better retrieval — not bigger models — is the key to trustworthy enterprise AI

As agentic and RAG systems move into production, retrieval quality is emerging as a quiet failure point — one that can undermine accuracy, cost, and user trust even when models themselves perform well ...
SiliconANGLE

Database company Weaviate speeds up AI development with flexible vector embeddings service

Dutch artificial intelligence database startup Weaviate B.V. is looking to streamline the data vectorization process with a new feature that automatically transforms unstructured information into ...
VentureBeat

Multimodal RAG is growing, here’s the best way to get started

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more As companies begin experimenting with ...
Tech Xplore on MSN

'Rosetta stone' for database inputs reveals serious security issue

The data inputs that enable modern search and recommendation systems were thought to be secure, but an algorithm developed by Cornell Tech researchers successfully teased out names, medical diagnoses ...
Geeky Gadgets

How to use Embeddings in RAG with Llama-Index

In the realm of natural language processing (NLP), the concept of embeddings plays a pivotal role. It is a technique that converts words, sentences, or even entire documents into numerical vectors.
Nasdaq

Elasticsearch Open Inference API now Supports Jina AI Embeddings and Rerank Model

Developers using Elastic to build search and RAG applications can now use the latest Jina AI embedding and reranking models without additional integration or development costs “Integrating Jina AI’s ...
InfoQ

Jina AI's Open-Source Embedding Model Outperforms OpenAI's Ada

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. In this episode, Thomas Betts chats with ...