Can dense, sparse and multi-vector approximate nearest neighbor search live in a single Rust codebase? Modular and Efficient Approximate Nearest Neighbors Search with Vectorium and kANNolo 🧩 Overview Two Rust libraries: Vectorium for data management and kANNolo for indexing. Encoders and datasets are compile-time type parameters, so each combination is specialized at build time. 🎯 The problem ・Faiss has a separate code path per index/quantizer combination, which is hard to extend ・Other libraries factor out only the graph or only the quantization ・None handles all three embedding types behind one interface 🛠 Method ・11 encoders (PQ, RaBitQ, sparse, multi-vector, and more) ・Hnsw and IVF, plus a two-stage RerankIndex that pairs any first-stage index with any rescoring ・A structure-of-arrays PQ table layout makes table building 4.5-5.2x faster 📊 Results ・Up to 3.26x hnswlib's throughput at 99% accuracy (kANNolo-Rerank, dense data) ・Smallest memory on every sparse collection ・But Glass wins on AgNews, Seismic is faster on sparse data, and builds are not fast #ANNSearch# #Rust#