kvex

Pure Erlang approximate k-nearest-neighbour vector search, powered by sied SIMD NIFs (AVX2 / AVX-512 / NEON).

No training phase — data-oblivious 1-bit quantization, vectors can be added at any time
Two-phase search — SIMD POPCNT Hamming filter → SIMD dot-product rerank
Flat binary cache — all vectors stored as a single ETS refc-binary; search never iterates over individual Erlang terms
Parallel reads — ETS protected table; concurrent BEAM processes query the same index safely
Pure Erlang index layer — only the SIMD kernels are in Rust (via sied)

Performance

10 000 vectors × dim=128, K=10, OTP 27, AVX2:

Operation	Latency
`add_batch/2` (10 000 vecs)	~13 ms
`search/3` avg	~60 µs
Throughput	~16 000 queries/s

Installation

{deps, [{kvex, "0.2.0"}]}.

No Rust toolchain required — sied bundles a pre-compiled NIF.

Quick start

{ok, Ix} = kvex:new(128),

%% Insert a single vector
Vec = [rand:uniform() || _ <- lists:seq(1, 128)],
ok  = kvex:add(Ix, 42, Vec),

%% Batch insert (more efficient for large loads)
Pairs = [{I, [rand:uniform() || _ <- lists:seq(1, 128)]}
         || I <- lists:seq(1, 10000)],
ok = kvex:add_batch(Ix, Pairs),

%% Top-10 nearest neighbours
{ok, Results} = kvex:search(Ix, Vec, 10).
%% Results = [{Id, Score}]  sorted descending

Vectors can be passed as little-endian f32 binaries (4 * Dim bytes), which avoids float-list traversal on hot paths:

VBin = << <<X:32/float-little>> || X <- Vec >>,
ok   = kvex:add(Ix, 43, VBin),
{ok, Top} = kvex:search(Ix, VBin, 10).

Cosine search

%% L2-normalize before indexing and querying for cosine similarity:
{ok, NormVec} = sied:l2_normalize_f32(Vec),
ok = kvex:add(Ix, 1, NormVec),
{ok, Results} = kvex:cosine_search(Ix, QueryVec, 10).

API

Function	Description
`new(Dim)` / `new(Dim, Opts)`	Create an empty index
`add(Ix, Id, Vector)`	Insert one vector — O(N) flat cache rebuild
`add_batch(Ix, Pairs)`	Batch insert — O(batch) flat binary append
`search(Ix, Query, K)`	Top-K by dot product (two-phase)
`cosine_search(Ix, Query, K)`	Like search but auto-normalizes query
`normalize(Vec)`	L2-normalize a vector
`size(Ix)`	Number of indexed vectors
`delete(Ix)`	Free ETS table

Types

-type id()     :: non_neg_integer() | binary().
-type vector() :: [float()] | binary().          %% binary = LE f32
-type opts()   :: #{bits => 2 | 3 | 4}.          %% bits option ignored in v0.2.0

Scoring

search/3 returns raw dot-product scores (higher = more similar). They are comparable within a single index but not calibrated to cosine or L2. For cosine semantics, L2-normalize your vectors before indexing and use cosine_search/3.

How it works

kvex maintains two structures in a single ETS table per index:

Per-vector records{Id, F32Bin, BinVec} — source of truth
Flat cache{flat, F32FlatBin, BvecFlatBin, IdsTuple} — all vectors concatenated into one refc-binary each

Search path (two NIF calls, no per-element Erlang term work):

sied:hamming_topk_flat/4   →  SIMD POPCNT on BvecFlat  →  top-C candidates
sied:dot_product_topk_flat/4  →  SIMD dot-product on F32Flat  →  top-K results

Default oversample factor: 10× (K=10 → 100 candidates for phase 1).

Building from source

rebar3 compile
rebar3 ct

License

Apache License 2.0 — see LICENSE.