Module index 

Indexing data structures for efficient nearest neighbor search.

This module provides approximate nearest neighbor search algorithms optimized for production ML workloads.

Algorithms

• HNSW (Hierarchical Navigable Small World): O(log n) approximate search

Quick Start

use aprender::index::hnsw::HNSWIndex;
use aprender::primitives::Vector;

// Create index with M=16 connections per node
let mut index = HNSWIndex::new(16, 200, 0.0);

// Add vectors at different angles (cosine distance measures angle)
index.add("horizontal", Vector::from_slice(&[1.0, 0.0, 0.0]));
index.add("diagonal", Vector::from_slice(&[1.0, 1.0, 0.0]));
index.add("vertical", Vector::from_slice(&[0.0, 1.0, 0.0]));

// Search for 2 nearest neighbors to nearly horizontal vector
let query = Vector::from_slice(&[0.9, 0.1, 0.0]);
let results = index.search(&query, 2);

assert_eq!(results.len(), 2);
// Results are sorted by cosine distance (closest first)
assert!(results[0].1 <= results[1].1);

Re-exports

pub use hnsw::HNSWIndex;

Modules

hnsw

Hierarchical Navigable Small World (HNSW) index.

Structs

BiEncoder

Bi-Encoder for efficient dense retrieval.

ColBERT

ColBERT-style late interaction retrieval.

CrossEncoder

Cross-Encoder for reranking search results.

HybridSearch

Hybrid Search combining dense and sparse retrieval.

Enums

SimilarityMetric

Similarity metric for comparing embeddings.

Functions

default_cross_encoder

Default cross-encoder using cosine similarity.