VentureBeat

New DeepMind study reveals a hidden bottleneck in vector search that breaks advanced RAG systems

Vector embeddings are the backbone of modern enterprise AI, powering everything from retrieval-augmented generation (RAG) to semantic search. But a new study from Google DeepMind reveals a fundamental ...
MarkTechPost

Google DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4

Google DeepMind's EmbeddingGemma 2 embeds text, code, images, video and audio in one 740M model for on-device RAG.
Help Net Security

Vector embedding security gap exposes enterprise AI pipelines

Enterprise adoption of retrieval-augmented generation has moved sensitive corporate content into a new storage format that existing security tools cannot inspect. Companies deploying internal AI ...
Indiatimes

Google CEO Sundar Pichai announces EmbeddingGemma 2 model; says it is company’s first open, natively multimodal embedding model

Google has launched EmbeddingGemma 2, an open multimodal embedding model for devices. The launch comes a year after the company introduced EmbeddingGemma for developers last year. For those unaware, ...
InfoWorld

Using PostgreSQL as a vector database in RAG

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 ...
Searchenginejournal.com

Introduction To Vector Databases And How To Use AI For SEO

Learn how to use vector databases for AI SEO and enhance your content strategy. Find the closest semantic similarity for your target query with efficient vector embeddings. A vector database is a ...