gemini-embedding-2

To embed multimodal data for cross-modal retrieval, you need a natively multimodal embedding model that processes text, images, audio, and video through a single transformer backbone into a unified vector space. This ensures accurate similarity computations across different content types.

MRL truncation allows you to dynamically reduce embedding dimensions from 3072 down to 128. This flexibility helps optimize memory usage and index sizing in your vector database without completely losing the semantic representation needed for similarity search.

Yes, you can use multimodal embeddings for RAG pipelines and semantic search by generating unified vector representations of your diverse data. These embeddings integrate seamlessly into vector databases to provide fast and accurate retrieval for agentic workflows.

No, you do not need separate models to generate embeddings for PDFs and video. A single-transformer, natively multimodal embedding model can process text, images, video, audio, and PDFs directly into the same 3072-dim vector space.

To calculate cosine similarity across text and image embeddings, both modalities must be mapped into a unified 3072-dim vector space by a single multimodal model. This shared space enables direct and accurate cross-modal similarity computations.

You should reduce embedding dimensions for clustering and classification when you need to optimize memory usage or speed up index sizing. Applying MRL truncation scales down the 3072-dim vectors to as low as 128 dimensions while maintaining functional semantic relationships.