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.

• Online ingest. Add vectors, they're indexed — no train step, no parameter tuning, no rebuilds as the corpus grows.
• Fast SIMD search. Hand-written kernels — NEON SDOT/SMMLA on ARM, AVX-512 VNNI and vpermb on x86, with AVX2 and scalar fallbacks — beat FAISS IndexPQFastScan in every measured config, averaging 3.4× at 4-bit and 23% at 2-bit across the eight cells of each width, on both architectures.
• Incremental saves. sync(path) persists just what changed since the last sync — one fsync per call, crash-safe at any byte, and a removal or a small append costs milliseconds however large the index. write/load stay for whole-file snapshots.
• Filter at search time. Pass an id allowlist (or a slot bitmask) to search() and the kernel honours it directly. You always get up to k results from the allowed set — no over-fetching, no recall hit on selective filters.
• Pure local. No managed service, no data leaving your machine or VPC. Pair with any open-source embedding model for a fully air-gapped RAG stack.

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

Python

pip install turbovec

from turbovec import TurboQuantIndex

index = TurboQuantIndex(dim=1536, bit_width=4)
index.add(vectors)
index.add(more_vectors)

scores, indices = index.search(query, k=10)

index.write("my_index.tv")
loaded = TurboQuantIndex.load("my_index.tv")

index.sync("my_index.tv")   # after more changes: durable incremental save

vectors and query are 2-D float32 arrays of shape (n, dim) — other dtypes are rejected rather than silently converted, so cast with np.asarray(x, dtype=np.float32) first if needed.

Need stable ids that survive deletes? Use IdMapIndex:

import numpy as np
from turbovec import IdMapIndex

index = IdMapIndex(dim=1536, bit_width=4)
index.add_with_ids(vectors, np.array([1001, 1002, 1003], dtype=np.uint64))

scores, ids = index.search(query, k=10)   # ids are your uint64 external ids
index.remove(1002)                         # O(1) by id

index.write("my_index.tvim")
loaded = IdMapIndex.load("my_index.tvim")

index.sync("my_index.tvim")   # durable incremental save, ids included

Hybrid retrieval (filtered search)

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

import numpy as np
from turbovec import IdMapIndex

idx = IdMapIndex(dim=1536, bit_width=4)
idx.add_with_ids(vectors, ids)

# Stage 1: external system narrows to candidate ids.
allowed = np.array(db.execute("SELECT id FROM docs WHERE tenant=?", (t,)).fetchall(),
                   dtype=np.uint64)

# Stage 2: dense rerank within the candidate set.
scores, ids = idx.search(query, k=10, allowlist=allowed)

Filtering happens inside the SIMD kernel at 32-vector block granula