ashvardanian/usearch-binary

Binary vector search example using Unum's USearch engine and pre-computed Wikipedia embeddings from Co:here and MixedBread

Binary Vector Search Examples for USearch

This repository contains examples for constructing binary vector-search indicies for WikiPedia embeddings available on the HuggingFace portal:

• Co:here
• MixedBread.ai

Running Examples

To view the results, check out the bench.ipynb. To replicate the results, first, download the data:

$ pip install -r requirements.txt
$ python download.py
$ ls -alh mixedbread | head -n 1
> total 15G
$ ls -alh cohere | head -n 1
> total 15G

In both cases, the embeddings have 1024 dimensions, each represented with a single bit, packed into 128-byte vectors. 32 GBs of RAM are recommended to run the scripts.

Optimizations

Knowing the length of embeddings is very handy for optimizations. If the embeddings are only 1024 bits long, we only need 2 ZMM registers to store the entire vector. We don't need any for-loops, then entire operation can be unrolled and inlined.

inline uint64_t hamming_distance(uint8_t const* first_vector, uint8_t const* second_vector) {
    __m512i const first_start = _mm512_loadu_si512((__m512i const*)(first_vector));
    __m512i const first_end = _mm512_loadu_si512((__m512i const*)(first_vector + 64));
    __m512i const second_start = _mm512_loadu_si512((__m512i const*)(second_vector));
    __m512i const second_end = _mm512_loadu_si512((__m512i const*)(second_vector + 64));
    __m512i const differences_start = _mm512_xor_epi64(first_start, second_start);
    __m512i const differences_end = _mm512_xor_epi64(first_end, second_end);
    __m512i const population_start = _mm512_popcnt_epi64(differences_start);
    __m512i const population_end = _mm512_popcnt_epi64(differences_end);
    __m512i const population = _mm512_add_epi64(population_start, population_end);
    return _mm512_reduce_add_epi64(population);
}

To run the kernel benchmarks, use the following command:

$ python kernel.py

To run benchmarks over real data:

$ python kernels.py --dir cohere --limit 1e6