Python App Environment Setup

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.

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").

Create your project folder:

mkdir my-app
cd my-app

Create a virtual environment:

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

Install essential packages:

pip install openai langchain faiss-cpu tiktoken chromadb

Optional (for web app):

pip install flask flask-cors

Create a .env file:

OPENAI_API_KEY=your_openai_key_here

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")

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.

Run your app:

python app.py

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               # [OK] 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.

Set Up Your Python App Environment