VectorSimilarity

This repo exposes C API for using vector similarity search. Allows Creating indices of vectors and searching for top K similar to some vector in two methods: brute force, and by using the hnsw algorithm (probabilistic).

The API header files are vec_sim.h and query_results.h, which are located in src/VecSim.

Algorithms

All of the algorithms in this library are designed to work inside RediSearch and support the following features:

1. In place insert, delete, and update vectors in the index.
2. KNN queries - results can be ordered by score or ID.
3. Iterator interface for consecutive KNN queries.
4. Range queries
5. Multiple vector indexing for the same label (multi-value indexing)
6. 3rd party allocators

Datatypes SIMD support

Operation	x86_64	arm64v8	Apple silicone
FP32 Internal product	SSE, AVX, AVX512	No SIMD support	No SIMD support
FP32 L2 distance	SSE, AVX, AVX512	No SIMD support	No SIMD support
FP64 Internal product	SSE, AVX, AVX512	No SIMD support	No SIMD support
FP64 L2 distance	SSE, AVX, AVX512	No SIMD support	No SIMD support

Flat (Brute Force)

Brute force comparison of the query vector q with the stored vectors. Vectors are stored in vector blocks, which are contiguous memory blocks, with configurable size.

HNSW

Modified implementation of hnswlib. Modified to accommodate the above feature set.

Build

For building you will need:

1. Python 3 as python (either by creating a virtual environment or setting your system python to point to the right python distribution)
2. gcc >= 10
3. cmake version >= 3.10

To build the main library, unit tests, and Python bindings in one command run

make

Unit tests

To execute unit tests run

make unit_test

Memory check

To run the unit tests with Valgrind run

make unit_test VALGRIND=1

Python bindings

Examples of using the Python bindings to run vector similarity search can be found in tests/flow. To build the Python wheel, first create a dedicated virtualenv using Python 3.7 and higher. Then, activate the environment, install the dependencies, and build the package. Please note, due to the way poetry generates a setup.py, you may have to erase it before re-running poetry build.

python -m venv venv
source venv/bin/activate
pip install poetry
poetry install
poetry build

To run in debug mode, replace the last two lines with:

DEBUG=1 poetry install
DEBUG=1 poetry build

After building the wheel, if you want to use the package you built, you will need to manually execute a pip install dist/.whl. Remember to replace with the complete package name.

Testing Python bindings

This will create a new virtual environment (if needed), install the wheel, and execute the Python bindings tests

poetry run pytest tests/flow

Or you can use the make command:

make flow_test

Benchmark

To benchmark the capabilities of this library, follow the instructions in the benchmarks user guide. If you'd like to create your own benchmarks, you can find more information in the developer guide.