Tools for Vector Databases

Vector Databases store and index high-dimensional embeddings—mathematical representations of data like text, images, or audio—to support fast and accurate similarity search. These databases are essential for applications in AI such as semantic search, recommendation systems, RAG (retrieval-augmented generation), anomaly detection, and clustering. They are optimized for nearest neighbor queries and often include features like hybrid search (text + vector), sharding, distributed querying, GPU acceleration, and integration with embedding models. Popular in use cases that require fast lookup across billions of vectors, they also offer APIs for ingestion, filtering, metadata management, and fine-tuned search configuration.