Today Google launches Gemini Embedding 2 in Public Preview, its first fully multimodal embeddings model built on the Gemini architecture. What does that mean for you or anyone working with diverse data? Basically, you can now map text, images, video, audio and documents into the same semantic space, in over 100 languages, without stitching together a bunch of complex pipelines.
What is Gemini Embedding 2
Gemini Embedding 2 turns different kinds of data into vectors that capture intent and meaning. Instead of one model for text, another for images and another for audio, everything lives in a single embedding space. Why is that useful? Because it makes tasks like semantic search, RAG (Retrieval-Augmented Generation), sentiment analysis and clustering with multimodal data much simpler.
The model is available in Public Preview via the Gemini API and Vertex AI, and works with popular tools like LangChain, LlamaIndex, Haystack, Weaviate, QDrant, ChromaDB and other vector search engines.