How to install Weaviate on Arch Linux

Weaviate is an open-source, cloud-native, vector-based knowledge graph that enables developers to build and manage Machine Learning applications. If you are interested in using Weaviate on Arch Linux, this guide will take you through the installation process.

Prerequisites

Before installing Weaviate, make sure your system meets the following prerequisites:

  • Arch Linux installed on your system
  • Root access to the system
  • Internet connectivity to download packages and dependencies

Installation

Step 1: Update the system

Before installing any new software, it is always recommended to first run an update on your system. To do this, open the terminal and execute the following command:

sudo pacman -Syu

Step 2: Install Go programming language

Weaviate is written in Go. Therefore, you need to install the Go programming language before proceeding with Weaviate installation. To install Go, run the following command in your terminal:

sudo pacman -S go

Step 3: Install Git

Weaviate is hosted on Github, and we will use Git to clone the project’s repository. To install Git, run the following command:

sudo pacman -S git

Step 4: Clone the Weaviate repository

Once Git is installed, create a new directory where you want to keep the Weaviate source code. 

mkdir weaviate && cd weaviate

Next, clone the Weaviate repository using the following command:

git clone [email protected]:semi-technologies/weaviate.git

This will clone the Weaviate repository into the current directory.

Step 5: Build Weaviate

Next, navigate into the cloned Weaviate repository:

cd weaviate

and build Weaviate from its source code using the following command:

make build

This will compile the Weaviate binary, which can be found in the dist directory.

Step 6: Running Weaviate

Once Weaviate is built, you can start it by running the following command:

./dist/weaviate

This will start Weaviate on the local machine.

Conclusion

Congratulations! You have installed Weaviate on your Arch Linux system. You can now start using Weaviate to build and manage your Machine Learning applications.