Cloud Computing

Retrieval Augmented Generation (RAG) is a powerful technique for enhancing large language models (LLMs) by grounding them in external knowledge sources. This blog post looks into a common challenge in RAG implementations: achieving high-quality semantic search. We’ll explore why traditional similarity search often falls short, and how new “task type” embeddings in Vertex AI offer

In today’s rapidly evolving technological landscape, businesses demand a database that delivers not only exceptional performance, but also embraces open standards and avoids vendor lock-in. Enter AlloyDB Omni, Google Cloud’s high-performance, 100% PostgreSQL-compatible downloadable database with cutting-edge capabilities that include a scalable online transaction processing (OLTP) architecture, near real-time analytics, and vector processing. And in

Over the course of the last few years, no technology has transformed how the world functions, or presented opportunities for advancement more than artificial intelligence (AI). At Google, AI is in our DNA. As a pioneer in AI, we have spent the last two decades leveraging AI to organize the world’s information, make it useful,

Google Cloud Storage is at the core of many customers’ cloud deployment because of its simplicity, affordability and near-infinite scale. But managing millions or billions of objects across numerous projects and with hundreds of developers can be complex, often requiring a team of analysts manually analyzing data for insights. Earlier this year, we introduced the

Startups are building the future of AI models, infrastructure, and applications on Google Cloud.  These fast-growing AI startups want to be able to accelerate their product development, ensure capacity, and scale cost-effectively. And they can with many of our unique offerings: a broad choice of Google-built, open, and partner models; our Vertex AI platform; access