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ruvnet
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499dec61a5 |
feat(ruvector-diskann): wire Quantizer trait into search path — codes load-bearing
Closes the architectural gap surfaced in PR #383: previously `DiskAnnIndex.pq_codes` was dead storage. `search()` called `graph.greedy_search` over `FlatVectors` (full f32 originals) and exact-L2² rerank — `self.pq_codes` was read by neither. Today's PQ "savings" were on-disk only; in DRAM the index still held full f32 vectors. RaBitQ inherited the same gap. This PR makes the `Quantizer` trait load-bearing in the DiskANN hot path so both backends (PQ + RaBitQ) actually consult their codes during graph traversal. ## What changes 1. **New `greedy_search_with_codes` in `graph.rs`** — generalizes `greedy_search_fast` to accept an arbitrary `distance_fn` closure over node ids. The original `greedy_search_fast` stays unchanged for back-compat. Surprise observation worth recording: the old `greedy_search_fast` was coupled to f32 distance via inline `l2_squared(...)` calls inside the loop, NOT via type signatures. The "abstraction gap" PR #383 left was three lines, not an architectural mismatch. 2. **`DiskAnnIndex` carries `enum QuantizerBackend`** rather than `Option<ProductQuantizer>`. Hybrid pattern chosen because: Option A (generic `DiskAnnIndex<Q>`) — would cascade through `ruvector-diskann-node`'s NAPI binding and any other crate using `DiskAnnIndex` by name. Too much ripple. Option B (`Box<dyn Quantizer>`) — impossible because `Quantizer::Query` is an associated type; trait isn't object-safe. Hybrid (this PR): match-once-per-search on the backend enum, dispatch into a monomorphic closure. Hot loop stays branch-free. 3. **Builder API**: `DiskAnnConfig::with_quantizer_kind()`, `with_rerank_factor()`, `with_rabitq_seed()`, `with_originals_in_memory()`. All have `Default` impls so existing call sites compile unchanged after a `..Default::default()` patch. 4. **`with_originals_in_memory(false)` plumbed but not yet honored**. `build()` rejects with `InvalidConfig` for now — the disk-backed rerank path that would land the actual 17.5× DRAM compression is the natural next PR. The trait-driven traversal shipped here is the prerequisite. Measured codes-vs-originals memory ratio at D=128, n=2000: **codes 40 KB, originals 1024 KB, ratio 0.039 (~25× smaller)** — once originals can be evicted, dataset-level compression will exceed the 17.5× target. ## Recall numbers | Path | recall@10 | Notes | |---|---|---| | Flat f32 (legacy) | 1.000 | n=1k, dim=64, 30 queries | | **PQ before this PR** | (≈ 1.000) | Codes never read — PQ was effectively no-op on recall | | **PQ after this PR** | 0.897 | Trait-driven, M=8, rerank_factor=4. Reflects actual quantization noise. | | **RaBitQ after this PR** | 0.967 | Trait-driven, search_beam=512, rerank_factor=40 | PQ recall now reflects real quantization noise because PQ codes are *used* during traversal. 0.897 is well above the 0.85 floor required by the regression test. ## NAPI binding patch `ruvector-diskann-node/src/lib.rs:48` was using a struct literal that listed every `DiskAnnConfig` field. Adding the four new fields broke that initializer. One-line `..Default::default()` fix restores it without growing the NAPI override surface (all new fields have sensible defaults). ## Verification cargo build --workspace → 0 errors cargo build -p ruvector-diskann --no-default-features → OK (PQ-only) cargo clippy --workspace --all-targets --no-deps -- -D warnings → exit 0 cargo fmt --all --check → exit 0 cargo test -p ruvector-diskann --features rabitq → 30 / 30 (was 26 in PR #383) New tests in `tests/quantizer_search_uses_codes.rs`: - spy-quantizer codes-consulted assertion (proves codes are now consulted; would fail before this PR) - recall@10 vs brute-force baseline (≥ 0.85 floor) - PQ recall regression (no drop vs the old "ignore codes, brute-force rerank" behavior, after accounting for actual quantization noise) - codes-vs-originals memory ratio sanity check - greedy_search_with_codes ≡ greedy_search_fast under f32 distance Refs: PR #383 (DiskANN Quantizer trait + RaBitQ backend), docs/research/rabitq-integration/05-roadmap.md Phase 1 Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruvnet
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96d8fdc172 |
chore(workspace): cargo fmt — mechanical whitespace fix across 427 files
Pre-existing rustfmt drift across the workspace was blocking CI's `Rustfmt` check on PR #373 + PR #377. Running plain `cargo fmt` reformats 427 files; no semantic changes, no logic changes, no behavior changes — just what rustfmt already wanted. None of the touched files are in ruvector-rabitq, ruvector-rulake, or the new mirror-rulake workflow — those were already fmt-clean per the per-crate checks on commits |
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rUv
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8fbe768629 |
feat(diskann): Vamana ANN + PQ + NAPI bindings — 14 tests, 1.0 recall, 90µs search (#334)
* 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> |