aiondb-vector

Vector type support for the engine. Centralises distance functions, index descriptors, quantization codecs, and the planner-side backend registry used by similarity search. The runtime VectorValue itself lives in aiondb-core and is re-exported here.

cargo

[dependencies]
aiondb-vector = { path = "../aiondb-vector" }

modules

module	purpose
`distance`	distance functions and the `VectorDistance` enum.
`index`	`VectorIndexDescriptor` and `VectorIndexAlgorithmParams`.
`planner`	`build_vector_search_plan` entry points.
`planner_backends`	backend trait, registry, and built-in `hnsw` / `ivf_flat` backends.
`quantization`	scalar, binary, and product quantization codecs.
`simd`	architecture dispatch for distance kernels (`x86`, `arm`, scalar fallback).
`types`	thin type helpers used by the planner.

key types

item	description
`VectorValue`	re-export of the core vector value (element type plus dims).
`VectorDistance`	`L2`, `Cosine`, `InnerProduct`, `Manhattan`.
`VectorIndexDescriptor`	catalog index id, algorithm, params, distance metric.
`VectorIndexAlgorithmParams`	`Hnsw(HnswParams)` or `Custom(BTreeMap<String, String>)`.
`VectorSearchAlgorithm`, `VectorSearchSpec`, `VectorSearchPlan`, `VectorDistanceMetric`	re-exports from `aiondb-plan`.
`VectorSearchBackend`, `VectorSearchBackendRegistry`	extension point for new ANN algorithms.
`QuantizationKind`	`None`, `Scalar`, `Binary`, `Product`.
`VectorQuantizer` trait	`encode`, `decode`, `approx_l2`.
`ScalarQuantizer` / `ScalarCode`	int8 scalar quantization.
`BinaryQuantizer` / `BinaryCode`	sign-bit binary quantization, packed `Vec<u64>`.
`ProductQuantizer` / `ProductCode`	product quantization with k-means subspace centroids.
`build_vector_search_plan`, `build_vector_search_plan_with_registry`	planner entry points.
`default_vector_search_backend_registry`	global registry pre-loaded with built-in backends.

distance functions

use aiondb_vector::distance::{
    cosine_distance, inner_product, l2_distance, manhattan_distance,
};

let a = [1.0_f32, 0.0, 0.0];
let b = [0.0_f32, 1.0, 0.0];

let _l2 = l2_distance(&a, &b);
let _cos = cosine_distance(&a, &b);
let _ip = inner_product(&a, &b);
let _l1 = manhattan_distance(&a, &b);

example

use aiondb_core::IndexId;
use aiondb_vector::{VectorDistance, VectorIndexDescriptor};

let descriptor = VectorIndexDescriptor::hnsw(
    IndexId::new(7),
    16,
    200,
    VectorDistance::Cosine,
);

assert!(matches!(descriptor.distance_metric, VectorDistance::Cosine));