The Excellence
Directory Platform Template

Commercial embedding models built for enterprise-grade semantic search and RAG applications. Features voyage-3 and voyage-3-large models with multimodal support. This is a commercial API service with usage-based pricing.

Maze is a web-scale video deduplication system that relies on large-scale approximate nearest neighbor vector search over video embeddings to detect and remove duplicate or near-duplicate videos efficiently. While not a general-purpose vector database, it represents a specialized, production-scale application of vector search infrastructure for multimedia content management.

A CVPR 2020 tutorial on large-scale image retrieval in unconstrained environments, including methods and system considerations for vector-based image search relevant to vector database and ANN applications.

Deep Lake is a vector database designed as a data lake for AI, capable of storing and managing vector embeddings, text, images, and videos. It utilizes a tensor format for efficient querying and integration with AI algorithms, making it suitable for similarity search and machine learning workflows. It is open-source and tailored for handling unstructured and multimodal data, with seamless integration with frameworks like PyTorch and TensorFlow.

An open-source library for creating, storing, and searching multimodal data and vector embeddings, supporting AI and ML workflows.

A course focused on mastering multimodal Retrieval Augmented Generation (RAG) and embeddings, which are fundamental components often stored and managed by vector databases.