Python Environment
It is time to transition from theory to practice. In this section, we will set up a clean, isolated Python workspace for our RAG project.
Keeping your project dependencies isolated is a fundamental best practice in data engineering and software development. We will achieve this using Python’s built-in virtual environments.
1. Create a Virtual Environment
Open your terminal, navigate to the root folder of your project (where you plan to write your code, outside of the docs folder), and run the following command to create a virtual environment named venv:
python -m venv venv
2. Activate the Environment
Before installing any packages, you must activate the environment. The command depends on your operating system:
- macOS and Linux:
source venv/bin/activate
- Windows (Command Prompt):
venv\Scripts\activate.bat
- Windows (PowerShell):
venv\Scripts\Activate.ps1
(You will know it worked if you see (venv) at the beginning of your terminal prompt).
3. Install Dependencies
With the environment activated, we need to install the required libraries. We will be using the official Weaviate v4 Python client, which is completely rewritten to be highly intuitive and strongly typed. We also need python-dotenv to securely manage our API keys.
Run this command:
pip install -U weaviate-client python-dotenv
Optional: If you want to use OpenAI’s LLMs for the final text generation step of our RAG pipeline, you should also install their official client:
pip install openai
4. Secure Your API Keys
Since Weaviate will be reaching out to external AI models (like OpenAI, Cohere, or Google) to generate vectors, it needs an API key. Never hardcode API keys directly into your Python scripts! If you push hardcoded keys to GitHub, they will be compromised immediately.
Instead, create a file named exactly .env in the root of your project folder. Add your keys there like this:
# .env file
OPENAI_API_KEY="sk-your-openai-api-key-here"
Next, create a .gitignore file in the same folder and add .env to it. This ensures Git will ignore your secrets.
We will use the python-dotenv library to load these variables securely into our Python scripts later.
✨ Want to use Google Gemini instead? (Click to expand)
If you prefer to use Google Gemini instead of OpenAI for your embeddings and text generation (which is a great choice and offers a generous free tier for developers!), the setup is very similar.
First, you might want to install the Google GenAI SDK (optional, but useful if you want to test the model outside of Weaviate):
pip install google-genai
Then, in your .env file, simply add your Gemini API key instead:
# .env file
GEMINI_API_KEY="your-gemini-api-key-here"
Later in the code, you will pass this key to Weaviate to enable its built-in Google integration modules (text2vec-google and generative-google).
Your local machine is now prepped and ready for coding! Next, we need to actually get our Vector Database up and running.