💻Client SDK
This guide provides instructions for installing the enVector SDK across different environments. Whether you're working on a local machine or in the cloud, we've got you covered.
Prerequisites
Python version requirements: Python
3.9 ≤ version ≤ 3.13pip (Python package installer)
virtualenv (recommended for isolated environments)
⚠️ Note: Intel-based macOS devices are not supported. If you have an Intel Mac, run a Linux environment inside a Docker container (e.g., Ubuntu via Docker Desktop) and follow the Linux installation steps there.
Installation by Platform
🐧 Linux (x86_64 & arm64)
This guide is for users on common Linux distributions with either x86_64 (AMD/Intel) or arm64 architectures.
1. Install System Dependencies (Python 3.9 ≤ version ≤ 3.13)
Linux distributions have different package managers depending on the distribution. The most reliable way to manage Python versions without interfering with system packages is to use pyenv.
If you don't have Python
3.9 ≤ version ≤ 3.13, we strongly recommend installing it with pyenv. This tool lets you install specific Python versions in your user directory.
a. Install pyenv build dependencies. Before installing pyenv, you need packages to build Python from source.
On Ubuntu/Debian:
sudo apt-get update && sudo apt-get install make build-essential libssl-dev zlib1g-dev \ libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \ libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-devOn Fedora/CentOS/RHEL:
sudo yum install gcc zlib-devel bzip2 bzip2-devel readline-devel sqlite \ sqlite-devel openssl-devel xz xz-devel libffi-devel
b. Install and configure pyenv. This script will install pyenv and set up the necessary shell environment variables.
c. Install Python 3.11 (recommended for best compatibility). With pyenv installed, you can now install Python 3.11.
ℹ️ Note: You can also use Python
3.9 ≤ version ≤ 3.13, but Python 3.11 is recommended for optimal performance and compatibility.
🍎 macOS (Apple Silicon)
This guide is for users on macOS with Apple Silicon chips (M1, M2, M3, etc.).
⚠️ Intel-based macOS devices are not supported. If you're using an Intel Mac, run a Linux environment inside a Docker container (for example, Docker Desktop running an Ubuntu image) and follow the Linux instructions inside that container.
1. Install System Dependencies
We'll use Homebrew to install required packages like Python and LibOMP. This greatly simplifies the setup process.
If you don't have Homebrew, run this command first:
Once Homebrew is ready, install the dependencies:
ℹ️ Note: If you prefer not to use Homebrew, you must manually install Python 3.11, virtualenv, and libomp using an alternative method.
☁️ Google Colab
Using the enVector SDK in Google Colab
This guide explains how to install and use the enVector SDK in a Google Colab environment. Note: Official support for Colab will begin with version 1.0.3, which is scheduled for release soon. Once available, you can easily install the SDK directly within a Colab notebook.
1. How to Install (After Version 1.0.3 is Released)
After the new version is published to PyPI, open a Google Colab notebook and run the following command in a code cell. The ! prefix executes shell commands, and specifying the version ensures you get the Colab-compatible release.
The [colab] suffix is an extra dependency specifier. It ensures that, in addition to the base pyenvector package, all Colab-specific dependencies are installed (e.g., additional packages or environment configurations needed for seamless operation in Google Colab).
✅
pyenvector[colab]→ Installs the base package along with synchronized Colab-specific dependencies, ensuring compatibility in the Colab environment.⚠️
pyenvector→ Installs only the base package with potentially newer dependency versions, which may cause certain Colab-related functionality to be unavailable.
2. Verify the Installation
After the installation is complete, you can import and use the SDK in any subsequent cell. To confirm it's working correctly, run the following code:
ℹ️ Note: Google Colab runs in an isolated environment, so you don't need to create a virtual environment or worry about package conflicts.
Common Installation Steps
Once you have Python and system dependencies installed on your platform, follow these common steps to complete the SDK installation.
ℹ️ Note: Google Colab users can skip this section as they only need to run the pip install command.
1. Create and Activate a Virtual Environment
Using a virtual environment prevents conflicts with other Python projects.
Install
virtualenv(if not already installed):Create the environment using your Python version:
Activate the environment:
Your terminal prompt will now be prefixed with
(envector_venv).
2. Install the enVector SDK
Finally, install the SDK from PyPI using pip. This command will download and install the pyenvector package inside your active virtual environment.
Verification
After installation, you can verify that the SDK is working correctly by running:
Next Steps
Once you have the SDK installed, you can:
Generate your API key following the Key Generation guide
Follow the Quick Start guide to begin using the SDK
Check out the API documentation for detailed usage examples
Troubleshooting
If you encounter any issues during installation:
Ensure you have a supported Python version installed:
Linux : Python
3.9 ≤ version ≤ 3.13macOS: Python
3.9 ≤ version ≤ 3.13
Colab environment
Make sure you include the Colab extras when installing:
Make sure you're using a virtual environment to avoid conflicts
Check that all system dependencies are properly installed
For Linux users, ensure pyenv is properly configured in your shell
For macOS users, verify that Homebrew and libomp are correctly installed
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