mirror of
https://github.com/ruvnet/RuVector.git
synced 2026-05-25 15:03:46 +00:00
0247c1fc2b
* feat(ruvector): implement missing capabilities (ADR-143) - speculativeEmbed: real FNV-1a hash embedding (128-dim) from file content - ragRetrieve: cosine similarity on embeddings + TF-IDF keyword fallback - contextRank: TF-IDF weighted scoring instead of raw keyword matching - Remove false DiskANN claim (will implement as Rust crate next) Co-Authored-By: claude-flow <ruv@ruv.net> * feat(diskann): Vamana graph + PQ — SSD-friendly billion-scale ANN (ADR-143) New Rust crate: ruvector-diskann Core algorithm (NeurIPS 2019 DiskANN paper): - Vamana graph with α-robust pruning (bounded out-degree R) - k-means++ seeded Product Quantization (M subspaces, 256 centroids) - Asymmetric PQ distance tables for fast candidate filtering - Two-phase search: PQ-filtered beam search → exact re-ranking - Memory-mapped persistence (mmap vectors + binary graph) Performance characteristics: - L2-squared distance with 8-wide loop unrolling (auto-vectorized) - Greedy beam search with bounded visited set - Save/load with flat binary format (mmap-friendly) 9 tests passing: distance, PQ train/encode, Vamana build/search, bounded degree, full index CRUD, PQ-accelerated search, save/load. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(diskann): NAPI-RS bindings + npm package + 14 tests passing Rust core (ruvector-diskann): - 4-accumulator L2 distance for ILP optimization - Recall@10 = 1.000 on 2K vectors - Search latency: 90µs (5K vectors, 128d, k=10) - 14 tests: distance, PQ, Vamana, recall, scale, edge cases NAPI-RS bindings (ruvector-diskann-node): - Sync + async build/search - Batch insert (flat Float32Array) - Save/load, delete, count - Thread-safe via parking_lot::RwLock npm package (@ruvector/diskann): - Platform-specific loader (linux/darwin/win) - TypeScript declarations - Node.js test passing Co-Authored-By: claude-flow <ruv@ruv.net> * ci(diskann): add cross-platform build + publish workflow 5 targets: linux-x64, linux-arm64, darwin-x64, darwin-arm64, win32-x64 Co-Authored-By: claude-flow <ruv@ruv.net> * perf(diskann): FlatVectors + VisitedSet + ILP + optional SIMD/GPU Optimizations applied: - FlatVectors: contiguous f32 slab (eliminates Vec<Vec> indirection) - VisitedSet: O(1) clear via generation counter (replaces HashSet) - 4-accumulator ILP for L2 distance (auto-vectorized) - Flat PQ distance table (cache-line friendly) - Parallel medoid finding via rayon - Zero-copy save (write flat slab directly) - Optional simsimd feature for hardware NEON/AVX2/AVX-512 - Optional gpu feature with Metal/CUDA/Vulkan dispatch stubs Results (5K vectors, 128d): - Search: 90µs → 55µs (1.6x faster) - Build: 6.9s → 6.2s (10% faster) - Recall@10: 0.998 (maintained) - 17 tests passing Co-Authored-By: claude-flow <ruv@ruv.net> --------- Co-authored-by: Reuven <cohen@ruv-mac-mini.local> |
||
|---|---|---|
| .. | ||
| agentic-synth-ci.yml | ||
| benchmarks.yml | ||
| build-attention.yml | ||
| build-diskann.yml | ||
| build-gnn.yml | ||
| build-graph-node.yml | ||
| build-graph-transformer.yml | ||
| build-native.yml | ||
| build-router.yml | ||
| build-rvf-node.yml | ||
| build-tiny-dancer.yml | ||
| build-verified.yml | ||
| copilot-setup-steps.yml | ||
| docker-publish.yml | ||
| edge-net-models.yml | ||
| hooks-ci.yml | ||
| postgres-extension-ci.yml | ||
| publish-all.yml | ||
| RELEASE-FLOW.md | ||
| release-rvf-cli.yml | ||
| RELEASE.md | ||
| release.yml | ||
| ruvector-postgres-ci.yml | ||
| ruvllm-benchmarks.yml | ||
| ruvllm-build.yml | ||
| ruvllm-native.yml | ||
| ruvltra-tests.yml | ||
| sona-napi.yml | ||
| sync-rvf-examples.yml | ||
| thermorust-ci.yml | ||
| validate-lockfile.yml | ||
| wasm-dedup-check.yml | ||