vector_kit 0.3.0

SIMD-accelerated vector math for embeddings: dot product, cosine similarity, normalization, and top-k search over packed matrices.

0.3.0

• The matrix search methods take a plain List<double> query, not only a Float32List. An embedding straight from a model is a List<double>, so building the index with fromRows and then calling topKCosine used to compile on the first line and fail on the second, which is exactly the kind of seam a caller trips on. The widening is source-compatible: a Float32List is a List<double>, so existing calls are unchanged. Applies to topKCosine, topKDot and topKEuclidean on both VectorMatrix and QuantizedMatrix.
• example/semantic_search.dart is the real use case: a 20,000-document index of 384-dim vectors, searched, with the result measured. A top-5 query is 1.4 ms against 15.9 ms for the hand-written cosine loop (11x), and the int8 QuantizedMatrix holds the index in a quarter of the memory, with the recall cost measured against the float ranking rather than assumed.

0.2.2

• Shorten the screenshot description. pub.dev accepts up to 200 characters but scores only those under 160, so the previous release published cleanly and quietly gave up the documentation points it was meant to earn.

0.2.1

• Declare the benchmark chart in pubspec.yaml so pub.dev renders it on the package page. The chart was already in the repository and the README, but pub.dev shows only what the screenshots: field points at, so the page a reader lands on from search opened with text where the measurement should have been.

0.2.0

• Add QuantizedMatrix, an int8 form of VectorMatrix for corpora that no longer fit comfortably in memory. Each row is scaled so its largest component maps to 127 and stored as one byte per dimension. Measured on 5,000 rows of 768 dimensions: 14.6 MB becomes 3.7 MB, 3.92x smaller, while a search goes from 0.63 ms to 2.50 ms a query, 3.96x the time, because the byte rows cannot use the SIMD path the float rows do. It buys memory and costs throughput, which is the trade to make only when the corpus is the problem.
• QuantizedMatrix.from leaves the source matrix usable, so recall can be measured against the exact ranking on real vectors. Recall@10 was 100% on the benchmark corpus, but that is an upper bound: uniformly random vectors sit far apart in high dimensions, and real embeddings cluster, which is where eight bits start confusing neighbours.

0.1.1

• Docs: tightened the README wording and visuals.

Changelog

0.1.0

• Initial release.
• SIMD dot product, cosine similarity, Euclidean distance, and normalization over Float32List, with fail-fast validation of lengths and non-finite components.
• VectorMatrix: packed row-major storage with precomputed row norms, top-k cosine, dot product, and Euclidean search, and a VKT1 binary format for serialization.