EmbeddingGemma

Google's first natively multimodal embedding model that maps text, images, video, audio and documents into a single embedding space. Supports over 100 languages with flexible output dimensions using Matryoshka Representation Learning.

Amazon Aurora Machine Learning provides managed vector storage and search capabilities integrated into Aurora PostgreSQL for AI workloads on AWS. Key features include serverless scaling, direct ML model calls via SQL for embeddings, and seamless integrations with Bedrock and SageMaker. Perfect for RAG pipelines and enterprise AI applications, it simplifies vectorization and abstracts infrastructure compared to self-hosted options like Milvus.

NVIDIA's generalist embedding model achieving record 69.32 score on MTEB benchmark. Fine-tuned from Llama architecture with improved techniques for training LLMs as embedding models.

Multi-Vector Retrieval Algorithm that reduces multi-vector similarity search to single-vector similarity search via Fixed Dimensional Encodings. Achieves 10% improved recall with 90% lower latency compared to existing approaches.

Combining dense vector embeddings with sparse representations in a single unified model. Captures both semantic meaning (dense) and exact term matching (sparse) for superior retrieval performance.

Retrieval-Augmented Generation extended to handle multiple modalities including text, images, video, and audio. Uses multimodal embeddings like Gemini Embedding 2 or CLIP to enable cross-modal search and generation.