Add MkDocs documentation

Covers: overview, setup, API reference, configuration,
testing, deployment, and known quirks.

docs/setup.md

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+# 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
+```