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Architecture and performance of Gemini Embedding 2, a native multimodal model that maps text, images, audio, and video into a single mathematical space.

Unlike traditional systems that rely on separate encoders or text transcriptions, this model uses bidirectional attention and direct sensory processing to preserve nuances like document layouts and vocal tones.

It employs Matryoshka Representation Learning, allowing developers to shrink vector sizes for efficiency without losing significant accuracy.

High-quality synthetic data and contrastive learning were used during training to ensure the model outperforms competitors in complex tasks like coding and cross-modal retrieval.

Real-world applications for this technology include multimodal RAG, where AI systems can simultaneously "read" text and "see" diagrams to answer user queries.

Ultimately, the sources highlight how this unified approach simplifies enterprise data infrastructure while establishing new benchmarks for zero-shot robustness across diverse scientific and creative fields.