# Configuration

All configuration is via environment variables, loaded through Pydantic Settings. Copy `.env.example` to `.env` and edit.

## Environment Variables

### SIP Trunk

| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `SIP_TRUNK_HOST` | Your SIP provider hostname | — | Yes |
| `SIP_TRUNK_PORT` | SIP signaling port | `5060` | No |
| `SIP_TRUNK_USERNAME` | SIP auth username | — | Yes |
| `SIP_TRUNK_PASSWORD` | SIP auth password | — | Yes |
| `SIP_TRUNK_DID` | Your phone number (E.164) | — | Yes |
| `SIP_TRUNK_TRANSPORT` | Transport protocol (`udp`, `tcp`, `tls`) | `udp` | No |

### Gateway

| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `GATEWAY_SIP_PORT` | Port for device SIP registration | `5080` | No |
| `GATEWAY_RTP_PORT_MIN` | Minimum RTP port | `10000` | No |
| `GATEWAY_RTP_PORT_MAX` | Maximum RTP port | `20000` | No |
| `GATEWAY_HOST` | Bind address | `0.0.0.0` | No |

### LLM

| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `LLM_BASE_URL` | OpenAI-compatible API endpoint | `http://localhost:11434/v1` | No |
| `LLM_MODEL` | Model name for IVR analysis | `llama3` | No |
| `LLM_API_KEY` | API key (if required) | `not-needed` | No |
| `LLM_TIMEOUT` | Request timeout in seconds | `30.0` | No |
| `LLM_MAX_TOKENS` | Max tokens per response | `1024` | No |
| `LLM_TEMPERATURE` | Sampling temperature | `0.3` | No |

### Speech-to-Text

| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `SPEACHES_URL` | Speaches/Whisper STT endpoint | `http://localhost:22070` | No |
| `SPEACHES_MODEL` | Whisper model name | `whisper-large-v3` | No |

### Database

| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `DATABASE_URL` | PostgreSQL or SQLite connection string | `sqlite+aiosqlite:///./hold_slayer.db` | No |

### Notifications

| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `NOTIFY_SMS_NUMBER` | Phone number for SMS alerts (E.164) | — | No |

### Audio Classifier

| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `CLASSIFIER_WINDOW_SECONDS` | Audio window size for classification | `3.0` | No |
| `CLASSIFIER_SILENCE_THRESHOLD` | RMS below this = silence | `0.85` | No |
| `CLASSIFIER_MUSIC_THRESHOLD` | Spectral flatness below this = music | `0.7` | No |
| `CLASSIFIER_SPEECH_THRESHOLD` | Spectral flatness above this = speech | `0.6` | No |

### Hold Slayer

| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `MAX_HOLD_TIME` | Maximum seconds to wait on hold | `7200` | No |
| `HOLD_CHECK_INTERVAL` | Seconds between audio checks | `2.0` | No |
| `DEFAULT_TRANSFER_DEVICE` | Device to transfer to | `sip_phone` | No |

### Recording

| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `RECORDING_DIR` | Directory for WAV recordings | `recordings` | No |
| `RECORDING_MAX_SECONDS` | Maximum recording duration | `7200` | No |
| `RECORDING_SAMPLE_RATE` | Audio sample rate | `16000` | No |

## Settings Architecture

Configuration is managed by Pydantic Settings in `config.py`:

```python
from config import get_settings

settings = get_settings()
settings.sip_trunk_host      # "sip.provider.com"
settings.llm.base_url        # "http://localhost:11434/v1"
settings.llm.model           # "llama3"
settings.speaches_url        # "http://localhost:22070"
settings.database_url        # "sqlite+aiosqlite:///./hold_slayer.db"
```

LLM settings are nested under `settings.llm` as a `LLMSettings` sub-model.

## Deployment

### Development

```bash
# 1. Clone and install
git clone <repo-url>
cd hold-slayer
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

# 2. Configure
cp .env.example .env
# Edit .env

# 3. Start Ollama (for LLM)
ollama serve
ollama pull llama3

# 4. Start Speaches (for STT)
docker run -p 22070:8000 ghcr.io/speaches-ai/speaches

# 5. Run
uvicorn main:app --host 0.0.0.0 --port 8000 --reload
```

### Production

```bash
# Use PostgreSQL instead of SQLite
DATABASE_URL=postgresql+asyncpg://user:pass@localhost/hold_slayer

# Use vLLM for faster inference
LLM_BASE_URL=http://localhost:8000/v1
LLM_MODEL=meta-llama/Llama-3-8B-Instruct

# Run with multiple workers (note: each worker is independent)
uvicorn main:app --host 0.0.0.0 --port 8000 --workers 1
```

Note: Hold Slayer is designed as a single-process application. Multiple workers would each have their own SIP engine and call state. For high availability, run behind a load balancer with sticky sessions.

### Docker

```dockerfile
FROM python:3.13-slim

# Install system dependencies for PJSUA2 and Sippy
RUN apt-get update && apt-get install -y \
    build-essential \
    libpjproject-dev \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app
COPY . .
RUN pip install -e .

EXPOSE 8000 5080/udp 10000-20000/udp

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
```

Port mapping:
- `8000` — HTTP API + WebSocket + MCP
- `5080/udp` — SIP device registration
- `10000-20000/udp` — RTP media ports