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

backend/services/pdf_processor.py

@@ -0,0 +1,45 @@
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+import os
+from typing import List, Dict
+from .embeddings import EmbeddingService
+
+class PDFProcessor:
+    def __init__(self, embedding_service: EmbeddingService):
+        self.embedding_service = embedding_service
+        self.text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=500,
+            chunk_overlap=50,
+            length_function=len,
+        )
+
+    def load_and_split(self, file_path: str, metadata: Dict) -> List[Dict]:
+        """Load and split a PDF file."""
+        loader = PyPDFLoader(file_path)
+        documents = loader.load()
+
+        chunks = self.text_splitter.split_documents(documents)
+
+        # Convert to dictionaries with metadata
+        chunk_data = []
+        for i, doc in enumerate(chunks):
+            chunk_data.append({
+                "content": doc.page_content,
+                "metadata": {
+                    **metadata,
+                    "chunk_id": i,
+                    "source": file_path,
+                }
+            })
+
+        return chunk_data
+
+    def embed_chunks(self, chunks: List[Dict]) -> List[Dict]:
+        """Add embeddings to chunks."""
+        texts = [chunk["content"] for chunk in chunks]
+        embeddings = self.embedding_service.embed(texts)
+
+        for chunk, embedding in zip(chunks, embeddings):
+            chunk["embedding"] = embedding.tolist()
+
+        return chunks