Gemini Embedding 2 offers a unified framework for embedding and retrieving multimodal data, including text, images, audio, videos and documents, within a shared vector space. As explained by Sam ...

While previous embedding models were largely restricted to text, this new model natively integrates text, images, video, audio, and documents into a single numerical space — reducing latency by as muc ...

MDB expands AI capabilities with new embedding models, vector search tools and APIs in Atlas, aiming to simplify AI app development as adoption accelerates.

Generative AI is revolutionizing data and analytics, but its applications demand advanced data management capabilities to handle vast, diverse, and complex datasets that include images, video, audio, ...

To harness the capabilities of these models, users can simply send a text string to the API endpoint and receive a numerical vector in return. This vector encapsulates the essence of the text’s ...

Vector databases and search aren’t new, but vectorization is essential for generative AI and working with LLMs. Here's what you need to know. One of my first projects as a software developer was ...

This expansion is fueled by the rapid adoption of AI, LLMs, and multimodal applications that require high-performance vector search, scalable indexing, and real-time retrieval. By offering, the ...