Google has released Gemini Embedding 2 , a new embedding model capable of integrating text and multimedia data, through Google AI Studio . This update provides the ability to map diverse data formats, such as text, images, video, audio, and PDFs, into a single, unified embedding space.
Previously, separate models were required for each data type, but this new model streamlines the technology stack by processing multimodal content within a single model. Specifically, when building a multimodal RAG (Retrieval Augmented Generation) system, it can simultaneously retrieve information from various file types, improving search accuracy and performance. Furthermore, it supports cross-modal search, enabling text queries to find relevant images, audio, and video clips within a single index.
Developers can immediately invoke the model via the Gemini API 's 'embed_content' method. Google recommends integration with major vector databases such as Vertex AI , Weaviate , Qdrant , and ChromaDB to efficiently store and index the generated high-dimensional embedding data.
This model is currently available as a preview version, and detailed technical specifications and usage instructions can be found in the official documentation .
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