SKVector

Semantic Search. Sovereign Speed.

Search that never leaves home. Your vectors, your hardware, zero cloud dependency.

Get Started View on GitHub

Vector embedding and similarity search. Qdrant backend, local-first embeddings, RAG pipeline support.

Features

Qdrant Backend

Local Embeddings

Similarity Search

RAG Pipeline Support

SKMemory Integration

Sovereign Speed

Get Started

$ pip install skvector

🐧 Own the Full Vertical

The modern stack is rented — your searches leave, your embeddings are stored somewhere else, your inference phones home. We rebuilt it from the ground up. SKVector is your Data / Semantic layer: vector search and embeddings that run on your hardware, answer your queries, and never leave your network.

Your data never leaves your hardware. Your keys, your disk, your rules.
Soul Apps Comms Models ▶ Data / Semantic — you are here Identity Security OS Silicon
See the whole vertical at skworld.io →

Shape the future → Submit proposals and vote at skarchitect.io