Building a Local-Only RAG System with Ollama and TypeScript
The article provides a tutorial on building a local-only Retrieval-Augmented Generation (RAG) system using Ollama and TypeScript. It emphasizes the benefits of keeping private documents on the user's machine without relying on third-party services. The tutorial outlines the steps to create a command-line tool that indexes files, answers questions, and cites sources, all while ensuring data privacy.
- ▪The tutorial allows users to build a RAG system that runs entirely on their local machine.
- ▪It uses a combination of Ollama, SQLite, and TypeScript to create a command-line tool.
- ▪Users can index .md or .txt files and ask questions in natural language without sending data to external servers.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 337213) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Pavel Espitia Posted on May 25 Building a Local-Only RAG System with Ollama and TypeScript #ai #ollama #typescript #tutorial Building a Local-Only RAG System with Ollama and TypeScript Most RAG tutorials send your private documents to OpenAI. Here's how to keep them on your laptop. This post walks through a complete Retrieval-Augmented Generation pipeline that runs entirely on your machine. No API keys, no third-party calls, no monthly bill.
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