Creating a simple local RAG system

We'll build a simple RAG system using local only models. We will not use LangChain, which is introducing many bloated dependencies, is much slower than direct Transformers usage, is not error-free and its documentation is mostly misleading. We'll use only bare Transformers functions for that. As a vector database for storing our embeddings from document, we'll use Faiss , which is really efficient in similarity search. Note it sits in RAM, not on a disk and is very fast. What is a RAG? Retrieval-Augmented Generation (RAG) is an AI framework that improves Large Language Model (LLM) accuracy by retrieving data from external, trusted sources (documents, databases) rather than relying solely on training data. It enables up-to-date, specialized answers, reduces hallucinations, and avoids costly model retraining. In simple words: it allows to have a LLM having a specialized knowledge without retraining it. We'll build here a simple version of it, allowing loading a single PDF files and then