# Build e Deployment Instructions

## Prerequisites

- Docker and Docker Compose installed
- ~5GB free disk space
- 8GB RAM minimum
- CPU for model inference

## Download the Model

Before building, download the Llama 3.2 3B quantized model:

```bash
# Download model (approximately 2GB)
cd backend
wget https://huggingface.co/MaziyarPanahi/Llama-3.2-3B-Instruct-GGUF/resolve/main/llama-3.2-3b-instruct-q4_0.gguf

# Place in models directory
mkdir -p models
mv llama-3.2-3b-instruct-q4_0.gguf models/
```

Alternatively, use GPT4All's built-in downloader when starting the service.

## Environment Setup

1. Copy the environment template:
```bash
cp backend/.env.example .env
```

2. Edit `.env` and set your Telegram credentials:
```env
TELEGRAM_BOT_TOKEN=your_bot_token_here
TELEGRAM_CHAT_ID=your_chat_id_here
```

To get your chat ID, create a bot with @BotFather on Telegram and send `/start`.

## Build and Start

1. Build the containers:
```bash
docker-compose build
```

2. Start all services:
```bash
docker-compose up -d
```

## Index the Books

After starting the services, index the 6 existing books:

```bash
docker-compose run --rm web python backend/scripts/index_books.py
```

This will:
- Load PDFs from the `PDF/` directory
- Extract text chunks
- Generate embeddings
- Store in Qdrant

## Access the Services

- **Web Interface**: http://localhost:8000
- **Qdrant API**: http://localhost:6333/dashboard
- **Redis**: localhost:6379

## Create a Telegram Bot

1. Chat with @BotFather on Telegram
2. Send `/newbot` and follow instructions
3. Copy the bot token to `.env`

## Schedule Messages

1. Open the web interface: http://localhost:8000
2. Click on "Agendar Mensagens"
3. Select date/time and optionally a specific book
4. Click "Adicionar Agendamento"

## Stop the Services

```bash
docker-compose down
```

## View Logs

```bash
# All services
docker-compose logs -f

# Specific service
docker-compose logs -f web
docker-compose logs -f celery
docker-compose logs -f celery-beat
```

## Troubleshooting

### Model Download Issues

If the model download fails:
```bash
# Download manually
cd backend/models
wget <model-url>
```

### Qdrant Not Starting

```bash
# Remove existing volume
docker volume rm espiritismo_qdrant_data
# Restart
docker-compose up -d qdrant
```

### Out of Memory

Reduce the number of workers or use a smaller model.

### Import Errors

Rebuild with:
```bash
docker-compose build --no-cache web
docker-compose up -d web
```