Google’s Gemini API introduces multimodal retrieval, allowing users to query both text and image data within a shared vector space. This capability supports complex use cases, such as analyzing PDFs ...

Google’s Gemini Embedding 2 processes multimodal data by embedding inputs like text, images and audio into a shared semantic space. This approach eliminates the need for separate transformations while ...

Conventional RAG systems are built mainly to retrieve text. Enterprise documents, however, often place critical facts in ...

Gemini 3 Pro reclaims AI benchmarks, outperforming previous models and dominating both LMArena and WebDev Arena leaderboards. New AI mode enhances Google Search with multimodal responses, offering ...

Google has officially launched Gemini 2.0, a significant update to its flagship AI model, aimed at enterprise users and developers seeking enhanced multimodal capabilities and improved performance.

Google’s newest AI model isn’t just another incremental upgrade. Having had early access to Gemini 3, I have been able to put it through its paces and it really delivers. With Gemini 3, the company is ...

Generative AI has rapidly moved from science fiction to everyday utility, transforming the way we work, learn, and create. In this evolving landscape, Google's multimodal AI platform, Gemini, stands ...

Google has launched Gemini 2.0, designed to empower enterprise users and developers with advanced multimodal capabilities and enhanced performance. The update aims to solidify Google's position as a ...

Learn what Google’s major AI models do, including Gemini, Veo, Imagen, Nano Banana, Gemma, Lyria, Chirp, and Gemini Nano.

Google’s Gemini model landing on Oracle Cloud marks a shift for enterprise AI adoption, bringing high-profile artificial intelligence out of Google’s exclusive orbit and into broader business ...