Building a RAG System That Runs Completely Offline

This guide shows how to build a fully offline Retrieval-Augmented Generation system that keeps sensitive documents on your machine. Using Ollama (Llama 3.2 for generation and nomic-embed-text for embeddings) plus FAISS for vector search, you’ll ingest …


This content originally appeared on HackerNoon and was authored by Tosin Kolawole

This guide shows how to build a fully offline Retrieval-Augmented Generation system that keeps sensitive documents on your machine. Using Ollama (Llama 3.2 for generation and nomic-embed-text for embeddings) plus FAISS for vector search, you’ll ingest PDFs/Markdown/HTML, chunk with overlap, embed locally, and answer questions with citations—no API keys, no usage fees, no data leaving your device after model downloads. The tutorial covers prerequisites, code for loaders/chunking/embeddings/vector DB/LLM, orchestration, and testing (FLoRA paper case study). Ideal for legal, medical, research, or enterprise teams that need strong privacy, predictable costs, and complete data control.


This content originally appeared on HackerNoon and was authored by Tosin Kolawole


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