i80Solutions.

Set Up Your Python App Environment

Follow these simple steps to get started with your Python application.

1. Install Python (version 3.10 or later):
   
   • Go to the official website: https://www.python.org/downloads/
   • Click the **“Download Python”** button for your operating system (Windows, macOS, or Linux).
   • Important: On Windows, during installation, make sure to check the box that says “Add Python to PATH” at the bottom of the setup window.
   • Then click **Install Now** and complete the setup.
   This ensures that you can use Python from the command line by simply typing python.
2. Install Visual Studio Code (VS Code):
   
   • Download from code.visualstudio.com
   • Launch VS Code after installing.
   • Go to the Extensions sidebar (or press Ctrl+Shift+X), search for Python, and install the extension by Microsoft.
   • Open your project folder in VS Code and select your Python interpreter (.venv/bin/python or .venv\Scripts\python.exe) via the Command Palette (Ctrl+Shift+P → “Python: Select Interpreter”).
3. Create your project folder:

   mkdir my-app
   cd my-app

4. Create a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # macOS/Linux
   .venv\\Scripts\\activate     # Windows

5. Install essential packages:

   pip install openai langchain faiss-cpu tiktoken chromadb

   Optional (for web app):

   pip install flask flask-cors

6. Create a .env file:

   OPENAI_API_KEY=your_openai_key_here

7. Install and configure a vector database (ChromaDB):
   
   • ChromaDB is a lightweight and easy-to-use vector database for local or cloud storage.
   • Already included if you installed chromadb in step 5.
   • Use persistent storage to retain data between runs:

     import chromadb
     client = chromadb.PersistentClient(path="./chroma-store")

8. Write your app logic (e.g. app.py):
   You don't need to write everything from scratch. You can ask an LLM like ChatGPT or Copilot to generate the code for you — all you need is the right prompt.
   
   Try this:
   Write Python code that uses OpenAI API to embed a user query,
search a ChromaDB vector store for the top 3 relevant documents,
and then send those results back to GPT-3.5 to generate an answer.
   
   This approach allows you to focus on what you want to build — not every syntax detail.
9. Run your app:

   python app.py

10. Organize your project structure:
    Once you've tested your basic app.py and confirmed it works, it's time to think about organizing your project for long-term development. Before writing serious logic or sharing your work on GitHub, a clean folder structure will make your code easier to scale, maintain, and collaborate on.

    
    my-app/
    ├── app/
    │   ├── api/                  # API route controllers
    │   ├── config/               # config files, etc.
    │   ├── service/             # Core business logic, Emebddings, storage, LLMs
    │   ├── ui/                   # UI-facing logic and assets
    │   │   ├── static/           # JS/CSS/images
    │   │   ├── templates/        # HTML templates
    │   │   └── app_controller.py  # (UI controller)
    │   ├── util/                # Logger, file utils, etc.
    │   └── main.py               # ✅ App entry point
    │
    ├── deploy/                   # AWS ECS, AppRunner, etc.
    ├── logs/               # Log files
    ├── test/                    # Pytest test suite
    ├── .dockerignore
    ├── .env                      # Environment config
    ├── .gitignore
    ├── Dockerfile                # For containerization
    ├── README.md
    └── requirements.txt
            

    This layout helps keep your logic modular and production-ready.