feat: implementar sistema RAG Espiritismo completo

- Stack: GPT4All + Llama 3.2 3B, LangChain, Qdrant, FastAPI, Docker
- 11 tarefas implementadas com 100% de conclusão
- Funcionalidades: chat com IA, upload PDF, bot Telegram, agendamento
- 6 livros PDF indexados (A Gênese, Livro dos Espíritos, etc.)
- Documentação rica: README, BUILD, task reports, design specs
- Deploy com Docker Compose
- Production ready

Tech stack:
- AI: GPT4All 2.4.6, Llama 3.2 3B q4_0
- Backend: FastAPI 0.115.0, Celery 5.4.0, Redis 5.2.0
- RAG: LangChain 0.3.7, Qdrant 1.11.1
- Frontend: HTML5/CSS3/Vanilla JS
- Deploy: Docker Compose

Acceptance Criteria:
✅ CPU-only, <8GB RAM, ~5GB storage
✅ Suporta upload de PDFs
✅ Interface web rica e responsiva
✅ Bot Telegram funcional
✅ Agendamento de mensagens
✅ Código limpo e documentado
✅ Deploy via Docker

Status: PRODUCTION READY
Version: 1.0.0
Date: 2026-11-01

BUILD.md

@@ -0,0 +1,134 @@
+# 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
+```