# Zero to Container: Syncing VS Code with Docker and uv

In modern AI development, consistency is everything. To build a robust **AI Analyst agent**, you need an environment that is isolated but accessible. By combining `uv`, Docker, and **Bind Mounts**, you can write code in your local VS Code while it executes instantly inside a high-performance container.

### Prerequisites

- Docker Desktop
- VS Code
- `uv` installed locally

## Phase 0: Getting `uv` on Your Machine

Before we containerize everything, you need `uv` installed locally to initialize your projects.

#### macOS / Linux

`curl -LsSf https://astral.sh/uv/install.sh | sh`

#### Windows (PowerShell)

`powershell -c "irm https://astral.sh/uv/install.ps1 | iex"`

## Step 1: Initialize the Local Project

Create your project folder and use `uv` to seed the basic Python structure.

```
mkdir ai-analyst-project && cd ai-analyst-project

uv init
```

**Note:** Rename the default `hello.py` to `main.py` to follow standard naming conventions.

## Step 2: Create the `Dockerfile`

This file defines your environment. We removed the `--frozen` flag to ensure `uv` can generate a lockfile during the first build.

```
FROM python:3.12-slim

# Install uv from the official binary
COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/

# Set performance and sync settings
ENV UV_LINK_MODE=copy \
    UV_COMPILE_BYTECODE=1 \
    PYTHONUNBUFFERED=1

WORKDIR /app

# Install dependencies first (better caching)
COPY pyproject.toml uv.lock* ./
RUN uv sync --no-install-project

# Copy project files
COPY . .
RUN uv sync
```

## Step 3: Create `docker-compose.yml`

This handles the volume mapping and keeps the container running with `tail -f /dev/null`.

```
services:
  agent:
    build: .
    container_name: ai_analyst_dev
    volumes:
      - .:/app
    env_file:
      - .env
    environment:
      - UV_LINK_MODE=copy
    tty: true
    stdin_open: true
    command: tail -f /dev/null
```

## Step 4: Configuration & Secrets

Create a `.dockerignore` file to prevent local junk from entering your container:

```.venv
__pycache__
.git```

Finally, create a `.env` file in your local folder to store your API keys:

```
GEMINI_API_KEY=your_secret_key_here
```

### Step 5: Start the Engine

Run these commands to build and enter your new development world:

- **1. Build & Start:** `docker-compose up -d --build`
- **2. Log In:** `docker exec -it ai_analyst_dev bash`

### Step 6: Verify the Live Sync

Confirm that your local VS Code is successfully talking to the container:

1. Open `main.py` in VS Code locally.
2. Change the code to: `print("Containerized Agent is Active!")`
3. In the Docker terminal you just logged into, run: `uv run main.py`

The changes appear instantly!