# Setup & Installation

## Prerequisites

- Python 3.12+
- [uv](https://astral.sh/uv/) package manager
- An LLM backend (Ollama, LM Studio, MiniMax, OpenAI, or any OpenAI/Anthropic-compatible API)

## Quick Start

```bash
# Clone the repository
git clone https://git.danhenry.dev/daniel/email-classifier.git
cd email-classifier

# Install dependencies
uv sync

# Start the server
uv run uvicorn app.main:app --host 0.0.0.0 --port 7999
```

The API will be available at `http://localhost:7999`. Auto-generated API docs are at `http://localhost:7999/docs` (Swagger UI) and `http://localhost:7999/redoc`.

## Environment Variables

The service is configured entirely through environment variables. See [Configuration](configuration.md) for the full reference.

A minimal `.env` file for local development with Ollama:

```bash
LLM_PROVIDER=openai
LLM_BASE_URL=http://localhost:11434/v1
LLM_API_KEY=none
LLM_MODEL=qwen2.5-7b-instruct.q4_k_m
LLM_TEMPERATURE=0.1
```

## Using Docker

```bash
# Build the image
docker build -t email-classifier .

# Run the container
docker run -p 7999:7999 \
  -e LLM_PROVIDER=openai \
  -e LLM_BASE_URL=http://host.docker.internal:11434/v1 \
  -e LLM_API_KEY=none \
  -e LLM_MODEL=qwen2.5-7b-instruct.q4_k_m \
  email-classifier
```

## Dependency Management

This project uses [uv](https://astral.sh/uv/) for dependency management. Do not use `pip` directly.

```bash
# Add a new dependency
uv add <package>

# Sync dependencies (after pulling changes)
uv sync

# Run with uv (recommended)
uv run uvicorn app.main:app --reload
```