Matryoshka-aware coarse-to-fine vector search: adaptive funnel ANN with measured recall and latency tradeoffs.

All major 2026 embedding models (OpenAI text-embedding-3, Nomic nomic-embed-text-v2, Voyage 4, Cohere v4, Jina v5) use Matryoshka Representation Learning (MRL): any prefix of the full-dimension vector is a valid, lower-dimensional embedding.

ruvector-matryoshka exploits this property for a coarse-to-fine search funnel:

Query at dim_coarse (e.g. 64)  →  cheap filter of the full index
       ↓
Re-rank shortlist at dim_full (e.g. 1536)  →  precise ranking

This gives 3–8× faster search with minimal recall loss versus full-dim search.

use ruvector_matryoshka::{CoarseFineFunnel, MatryoshkaConfig};

let cfg = MatryoshkaConfig {
    full_dim: 1536,
    coarse_dim: 64,
    oversample: 10,    // fetch 10× at coarse stage, re-rank to k
    m: 16,
    ef_construction: 100,
    ef_search: 64,
};

let mut idx = CoarseFineFunnel::new(cfg);

// Insert: only stores the full vector; coarse index built from prefix
idx.insert(0, vec![0.1_f32; 1536]);
idx.insert(1, vec![0.2_f32; 1536]);

// Search: coarse-to-fine funnel
let results: Vec<(u64, f32)> = idx.search(&[0.15_f32; 1536], 10);

Run cargo run --release -p ruvector-matryoshka --bin benchmark for live numbers.

Any model trained with MRL or returning truncatable embeddings:

• OpenAI text-embedding-3-small / text-embedding-3-large
• Nomic nomic-embed-text-v2
• Voyage 4 series
• Cohere embed-v4
• Jina jina-embeddings-v5

MIT — part of the RuVector project.