vicinity 0.3.6

Approximate nearest-neighbor search
Documentation

vicinity

crates.io docs.rs

Approximate nearest-neighbor search.

Install

Each algorithm is a separate feature. Enable what you need:

[dependencies]
vicinity = { version = "0.3", features = ["hnsw"] }          # graph index
# vicinity = { version = "0.3", features = ["ivf_pq"] }      # compressed index
# vicinity = { version = "0.3", features = ["nsw"] }         # flat graph

Usage

HNSW

High recall, in-memory. Best default choice.

use vicinity::hnsw::HNSWIndex;

let mut index = HNSWIndex::builder(128).m(16).ef_search(50).build()?;
index.add_slice(0, &[0.1; 128])?;
index.add_slice(1, &[0.2; 128])?;
index.build()?;

let results = index.search(&[0.1; 128], 5, 50)?;
// results: Vec<(doc_id, distance)>

IVF-PQ

Compressed index. 32–64× less memory than HNSW, lower recall. Use for datasets that don't fit in RAM.

use vicinity::ivf_pq::{IVFPQIndex, IVFPQParams};

let params = IVFPQParams { num_clusters: 256, num_codebooks: 8, nprobe: 16, ..Default::default() };
let mut index = IVFPQIndex::new(128, params)?;
index.add_slice(0, &[0.1; 128])?;
index.add_slice(1, &[0.2; 128])?;
index.build()?;

let results = index.search(&[0.1; 128], 5)?;

Benchmark

GloVe-25 (1.18M vectors, 25-d, cosine), Apple Silicon, single-threaded:

Full numbers in docs/benchmark-results.md.

Algorithms

Each algorithm has a named feature flag:

Algorithm Feature Notes
HNSW hnsw (default) Best recall/QPS balance for in-memory search
SymphonyQG hnsw + ivf_rabitq HNSW with RaBitQ quantized graph traversal; cheap approximate beam search + exact rerank
NSW nsw Flat small-world graph; competitive with HNSW on high-d data
Vamana vamana DiskANN-style robust pruning; fast search, higher build time
NSG nsg Monotonic RNG pruning; 50K cap due to O(n) ensure_connectivity
EMG emg Multi-scale graph with alpha-pruning
FINGER finger Projection-based distance lower bounds for search pruning
PiPNN pipnn Partition-then-refine with HashPrune; reduces I/O during build
FreshGraph fresh_graph Streaming insert/delete with tombstones
IVF-PQ ivf_pq Compressed index; 32-64x less memory, lower recall
IVF-AVQ ivf_avq Anisotropic VQ + reranking; inner product search
IVF-RaBitQ ivf_rabitq RaBitQ binary quantization; provable error bounds
RpQuant rp_quant Random projection + scalar quantization
BinaryFlat binary_index 1-bit quantization + full-precision rerank
Curator curator K-means tree with per-label Bloom filters; low-selectivity filtered search
FilteredGraph filtered_graph Predicate-filtered graph search (AND/OR metadata filters)
ESG esg Range-filtered search over numeric attributes
SparseMIPS sparse_mips Graph index for sparse vectors (SPLADE/BM25)
DiskANN diskann Vamana + SSD I/O layout; experimental
SNG sng Small navigable graph; O(n^2) construction
DEG hnsw Density-adaptive edge budgets (submodule of hnsw); O(n^2)
KD-Tree kdtree Exact NN; fast for d <= 20
Ball Tree balltree Exact NN; slightly better than KD-Tree for d=20-50
RP-Forest rptree Approximate; fast build, moderate recall

Quantization: RaBitQ, SAQ (quantization feature, via qntz crate). PQ is part of ivf_pq.

See docs.rs for the full API.

License

MIT OR Apache-2.0