Yesterday amid a flurry of enterprise AI product updates, Google announced arguably its most significant one for enterprise customers: the public preview availability of Gemini Embedding 2, its new embeddings model — a significant evolution in how machines represent and retrieve information across different media types. 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 much as 70% for some customers and reducing total cost for enterprises who use AI models powered by their own data to complete business tasks.Who needs and uses an embedding model?For those who have encountered the term “embeddings” in AI discussions but find it abstract, a useful analogy is that of a universal library. In a traditional library, books are organized by metadata: author, title, or genre. In the “embedding space” of an AI, information is organized by ideas.Imagine a library where books aren’t organized by the Dewey Decimal System, but by their “vibe” or “essence”. In this library, a biography of Steve Jobs would physically fly across the room to sit next to a technical manual for a Macintosh. A poem about a sunset would drift toward a photography book of the Pacific Coast, with all thematically similar content organized in beautiful hovering “clouds” of books. This is basically what an embedding model does.An embedding model takes complex data—like a sentence, a photo of a sunset, or a snippet of a podcast—and converts it into a long list of numbers called a vector. These numbers represent coordinates in a high-dimensional map. If two items are “semantically” similar (e.g. …
