Building a Lightweight Local RAG System: A Practical Workflow
Building a Lightweight Local RAG System: A Practical Workflow This article outlines a reproducible method to build a simple retrieval-augmented generation (RAG) system on a constrained machine. The goal is to combine compact embeddings, a minimal vector index, and a small quantized language model to create a functional question–answer pipeline. Objective Create a local RAG setup that runs efficiently on CPU or on a small GPU (6–8 GB), with predictable latency and no external services. The workflow avoids large dependencies and focuses on core components only. System Requirements Component Minimum CPU Any modern laptop GPU (optional) 6–8 GB VRAM Python 3.10 or 3.11 Disk 2–3 GB free Architecture Overview Embedding Model: small CPU-friendly model for document vectorization Index: lightweight FAISS or SQLite-based store LLM: 4-bit quantized model for question answering Pipeline: retrieve → format → generate Environment Setup cat <<'Eof' ...