Feder

This paper introduces the HNSW algorithm, which is widely adopted in vector databases and search engines for its efficient and robust performance on high-dimensional data. HNSW is foundational in powering modern vector search systems.

Built-in GUI introduced in Milvus v2.5 for system observability, offering real-time monitoring of system health, collection management, and query optimization from a unified dashboard.

Enterprise-grade desktop application for managing and analyzing vector databases with interactive visualizations, supporting Qdrant, Weaviate, Milvus, ChromaDB, Pinecone, pgvector, and Elasticsearch.

Computer vision interface for vector search with native integrations for Qdrant, Pinecone, LanceDB, and Milvus. Enables natural language search, configurable vector database backends, and visualization of search matches across billions of images.

AI-ready data visualization platform for massive datasets of embeddings. Atlas enables interactive exploration of millions of vectors in your web browser, with automatic dimensionality reduction and semantic clustering.