Build a RAG System with Python and a Local LLM (No API Costs)

Build a RAG System with Python and a Local LLM (No API Costs) RAG (Retrieval-Augmented Generation) is the most in-demand LLM skill in 2026. Every company wants to point an AI at their docs, their codebase, their knowledge base — and get useful answers back. The typical stack involves OpenAI embeddings + GPT-4 + a vector DB. The typical bill involves a credit card. Here's how to build the same thing entirely on local hardware : Python + Ollama + ChromaDB . No API keys. No per-token costs. Runs on a laptop or a home server. What We're Building A RAG pipeline that: Ingests documents (text files, markdown, PDFs) Embeds them using a local model Stores vectors in ChromaDB (local, in-memory or persistent) Retrieves relevant chunks on query Generates an answer using a local LLM via Ollama Total cloud cost: $0. Prerequisites Python 3.10+ Ollama installed with at least one model pulled 8 GB RAM minimum (16 GB recommended for 14B models) # Install dependencies pip install chromadb ollama requests