v0.1.1

vecq-core 0.1.1

First public release of vecq on crates.io. vecq is a training-free 4-bit vector quantization and search library written in pure Rust with zero dependencies, built for on-device and edge workloads where index size and build simplicity matter more than last-millisecond latency.

[dependencies]
vecq-core = "0.1.1"

Highlights

• 4-bit quantization, no training - quantize float32 vectors to 4-bit codes with no calibration dataset, no training pass, no model artifacts. Add vectors, search, persist, load.
• 6x smaller index - 514 bytes per 768-dim vector vs 3072 bytes for f32 (5.98x compression).
• Zero dependencies - cargo add vecq-core pulls exactly one crate. No C++ toolchain, no transitive deps, compiles anywhere Rust does.
• Deterministic results - bit-identical rankings across every ARM64 device (no FMA contraction, no platform-dependent reordering).
• ARM64 NEON path - 4-vector batch scoring with bounded-heap top-k for flat, predictable scan latency.

Benchmark

768-dim EmbeddingGemma embeddings, 2000 index vectors, 100 queries, ARM64. Full methodology in docs/BENCHMARK.md.

Metric	vecq	f32 brute force	usearch
Bytes per vector	514	3072	-
Compression	5.98x	1x	-
Query latency	0.89 ms	-	0.23 ms
Recall@10	0.958	1.000	0.995

What's Changed

Added

• Release pipeline: push a vX.Y.Z tag on main to publish vecq-core to crates.io, verify the version via the crates.io API, and create the GitHub Release.

Full changelog: v0.1.0...v0.1.1

When to use vecq

Use it when the index lives on-device, dimensions are a few hundred to a thousand, the corpus is in the tens of thousands, and you value small files and simple builds. For server-side workloads at scale, use a full ANN index (Qdrant, usearch) and keep vecq for the edge.