2 commits

Author	SHA1	Message	Date
rUv	019e5afff3	research(nightly): ACORN — predicate-agnostic filtered HNSW (#391) ... * docs(adr): add ADR-160 for ACORN predicate-agnostic filtered HNSW Records the decision to ship ruvector-acorn as the ruvector solution for filtered vector search recall collapse at low predicate selectivity. Documents 3 concrete index variants, measured benchmark results, consequences, and a 4-phase implementation roadmap (NN-descent, payload index, delta-index, SIMD). https://claude.ai/code/session_0173QrGBttNDWcVXXh4P17if * docs(research): add nightly research doc — ACORN filtered HNSW (2026-04-26) Full research document: SOTA survey (SIGMOD 2024, competitor changelog), proposed design with graph construction + ACORN beam search pseudocode, implementation notes (greedy vs NN-descent, entry point selection, predicate generality), real benchmark methodology and results table, blog-readable walkthrough, failure modes, roadmap, and production crate layout proposal. https://claude.ai/code/session_0173QrGBttNDWcVXXh4P17if --------- Co-authored-by: Claude <noreply@anthropic.com>	2026-04-27 00:29:37 -04:00
Claude	f2dbb6efbd	feat(rabitq): add RaBitQ rotation-based 1-bit quantization crate (ADR-154) ... Implements SIGMOD 2024 RaBitQ algorithm as ruvector-rabitq crate: - RandomRotation: Haar-uniform D×D orthogonal matrix via Gram-Schmidt - BinaryCode: u64-packed sign bits + XNOR-popcount + angular correction estimator - AnnIndex trait with 3 swappable backends (FlatF32, RabitqIndex, RabitqPlusIndex) Measured on x86-64, D=128, Gaussian-cluster data (100 clusters, σ=0.6): - RaBitQ+ rerank×5: 98.9% recall@10 at 4,271 QPS (2.05× vs exact 2,087 QPS) - RaBitQ+ rerank×10: 100.0% recall@10 at 4,069 QPS (1.95×) - Memory: 17.5× compression (1.4 MB vs 24.4 MB at n=50K, D=128) - Binary codes: 16 bytes/vec (2 u64) vs 512 bytes (f32) at D=128 All 10 unit tests pass. cargo build --release succeeds. https://claude.ai/code/session_01DAaNhfoLwpbWRbExsayoep	2026-04-23 07:56:23 +00:00