# VectorSimilarity

**Repository Path**: xiaonian0430/VectorSimilarity

## Basic Information

- **Project Name**: VectorSimilarity
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-22
- **Last Updated**: 2021-12-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[![CircleCI](https://circleci.com/gh/RedisAI/VectorSimilarity/tree/main.svg?style=svg)](https://circleci.com/gh/RedisAI/VectorSimilarity/tree/main)
[![codecov](https://codecov.io/gh/RedisAI/VectorSimilarity/branch/main/graph/badge.svg)](https://codecov.io/gh/RedisAI/VectorSimilarity)
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[![CodeQL](https://github.com/RedisAI/VectorSimilarity/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/RedisAI/VectorSimilarity/actions/workflows/codeql-analysis.yml)
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# 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](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.vscode-remote-extensionpack]) 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](https://ohmyz.sh/) 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/<package>.whl*. Remember to replace <package> 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
```