Phase II · High-Velocity Concurrent Processing Tier
Concurrent nearest neighbour over distributions, in Rust.
Concurrent HNSW with a pluggable distance metric: the first production-ready Sliced-Wasserstein implementation that composes cleanly with HNSW. AGPL-3.0 + Commercial.
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Metric
SW₁
Candidates eval.
50
Nearest dist.
0.0866
Recall@5
100%
What you get
- →vector-index: generic ConcurrentHnsw<P, M> over a parking_lot::RwLock-backed index.
- →sliced-wasserstein: SW₁ over point-cloud fingerprints; implements Metric from vector-index behind a feature flag.
- →Stable insert/search under high-throughput concurrent ingest.
What it solves
- Cosine and L2 collapse semantically rich fingerprints; SW₁ preserves distributional structure.
- HNSW gives you fast k-NN. Compose the two for a large-scale, distributionally-aware index.
Pricing
AGPL-3.0: free for research & open source
Enterprise use cases
Catalogue dedup / matching
Stock-audio and stock-video libraries at multi-billion-asset scale.
AI-content attribution
Find the closest known fingerprint to a freshly generated piece of media.
Recommendation over empirical distributions
When the 'item' is a behavioural histogram, not a point in space.
Quickstart
use vector_index::{HnswConfig, concurrent::ConcurrentHnsw};
use sliced_wasserstein::{PointCloud, SlicedWasserstein, SwConfig};
let metric = SlicedWasserstein::new(SwConfig { dim: 172, n_projections: 100, seed: 42 });
let index = ConcurrentHnsw::new(HnswConfig::default(), metric)?;
index.insert(point_id, PointCloud::new(fp, 172)?)?;
let hits = index.search(&query, 10);