The Excellence
Directory Platform Template

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.

Managed vector search service as part of Google Vertex AI, enabling efficient similarity search over high-dimensional vectors for AI applications.

Scalable Nearest Neighbors library by Google Research that provides efficient vector similarity search at scale. Uses anisotropic vector quantization and advanced compression techniques to handle twice as many queries per second compared to alternatives.

Google's text embedding model based on the Gemma architecture, available through Ollama and other platforms. Designed for generating high-quality embeddings for semantic search, retrieval, and various NLP tasks with efficient resource utilization.

A major algorithmic advancement to Google's ScaNN that introduces controlled redundancy to the vector index, leading to improved search efficiency. Enables even faster vector search while maintaining or improving accuracy.

Vector index type based on Google's ScaNN algorithm combining tree-like structure with Asymmetric Hashing quantization, optimized for batch queries with 10x faster index generation and smaller memory footprint.

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.