wtf is vec?

VEC is a vector database, a database designed for storing and searching AI-generated numerical representations called embeddings, that runs entirely on your GPU's video memory (VRAM). By keeping all data in VRAM rather than on disk, it achieves very fast similarity searches: finding the nearest matching vectors in a set of one million takes around 14 milliseconds on an RTX 3060. You launch it with a single executable, give it a name and a dimension count, and it starts listening on a network port. No separate database server, no configuration files. A CPU-only version called vec-cpu is also included for systems without a compatible GPU. Supported GPUs are NVIDIA RTX 2000 series and newer, AMD and Intel GPUs are not supported. Each stored record holds a vector, an optional label, and an optional data payload of up to 100 kilobytes. The database supports 16 commands including storing vectors, nearest-neighbor search by either L2 (Euclidean distance) or cosine similarity, exact-match lookup, clustering via a DBSCAN algorithm, and farthest-point sampling. All communication uses a binary protocol over TCP, named pipe, or Unix socket, there is no text interface. Client libraries are provided for C++, Python, Node.js, and Delphi. Every query is a brute-force scan of all stored vectors, which guarantees exact results with no approximation. Duplicate vectors are rejected at write time using a hash comparison. Data lives entirely in memory, disk is only used on startup and when you explicitly save. The fp16 storage option halves VRAM usage at the cost of some precision, effectively doubling capacity.