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 hnsw algorithm (probabilistic).

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

Algorithms

Flat (Brute Force)

TBD

HNSW

TBD

Build

For building you will need:

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

To build the main library, unit tests and python byndings in one command run

make

Unit tests

To execute unit tests run

make
make unit_test

Memory check

To run unit with valgrind run

make VALGRIND=1
make unit_test VALGRIND=1

To run unit tests with clang memory and address santizersm, we have provided you a docker image to do so.

VSCode devcontainer

If you are using VSCode, make sure you have the Visual Studio Code Remote Development Extension Pack installed. You will be prompt about opening the repository in the devcontainer. This will automatically setup a development container with all the dependencies installed and will use Oh My ZSH as shell.

When done, execute below for memory sanitizer

make
make unit_test SAN=memory

Or execute below for address sanitizer

make
make unit_test SAN=address

Docker build

Build and run the docker image

docker build .devcontainer -t vecsim_sanitizer 
docker run -it -v "$(pwd)":/project vecsim_sanitizer 

When on docker, you will need to install the dependencies in order to build and run tests

$ cd /project
$ ./sbin/system-setup.p

When done, execute below for memory sanitizer

make
make unit_test SAN=memory

Or execute below for address sanitizer

make
make unit_test SAN=address

Python bindings

Examples for 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 3.6 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 prior to 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 tox environment, install the wheel and execute the python bindings tests

tox -e flowenv