RyanCodrai/turbovec

A 10 million document corpus takes 31 GB of RAM as float32. turbovec fits it in 4 GB - and searches it faster than FAISS.

turbovec is a Rust vector index with Python bindings, built on Google Research's TurboQuant algorithm — a data-oblivious quantizer with near-optimal distortion and no separate training phase.

Building RAG where privacy, memory, or latency matters? You're in the right place.

Restrict results to a candidate set produced by another system (SQL, BM25, ACL, time window, …):

Filtering happens inside the SIMD kernel at 32-vector block granularity: blocks with no allowed slots are short-circuited before any LUT lookup or scoring work, and individual non-allowed slots inside scored blocks are dropped at heap-insert. Selective allowlists (small fraction of the index allowed) therefore avoid most of the SIMD cost rather than paying it and discarding the result afterwards.

The output length is min(k, len(allowed)) — when the allowlist is smaller than k you get exactly len(allowed) results rather than padded fallbacks.

See docs/api.md for the full reference.