What is the main purpose of this guide?

This guide teaches beginners how to create a local AI that runs on their own computer and uses their own documents to answer questions. It focuses on a hands-on approach without requiring prior programming experience.

What is RAG and how does it relate to this guide?

RAG, or Retrieval Augmented Generation, is the method used in this guide to answer questions. It automatically provides the AI with relevant information from your documents, eliminating the need for training the AI model.

What hardware requirements are recommended for running a local AI?

For optimal performance, a CPU with at least 32GB of RAM is recommended, especially if using a CPU only setup. For GPU acceleration, a NVIDIA GPU with 6GB of VRAM or more is ideal, with Apple Silicon Macs offering excellent unified memory performance for running local AI models.

How do I install Python?

You must download the latest version of Python from python.org and follow the installer instructions, ensuring you tick the 'Add Python to PATH' option.

What are gpt4all and chromadb?

Gpt4all is the local AI model, and chromadb is a database that helps the AI understand and search your text documents effectively. They work together to enable you to ask questions and get answers based on your data.

How do I add new documents to the AI?

Simply place new .txt files into the 'documents' folder, then delete the 'database' folder and restart the script. The AI will automatically rebuild the database with your updated documents.

What is RAG and how does it work with a local AI?

RAG, or Retrieval Augmented Generation, allows a local AI to answer questions by first searching your documents for relevant passages. These passages, along with your original question, are then fed to the AI for a customized response, ensuring the answer is based solely on your data.

What are some recommended tools for extending a local AI with my own data?

Popular tools include pypdf2 and python-docx for handling PDF and Word documents, as well as Ollama and LM Studio, which offer easy installation and model access. These tools work in conjunction with a document database like ChromaDB.

Local AI with Your Own Data: Step-by-Step Guide

A step-by-step guide for beginners

What if your AI knew only your documents, and still found the right answer in a flash? No cloud, no subscription fees, no strangers looking at your data. In this guide you build exactly that: a local AI that runs on your own computer and answers questions about your own content. Step by step, even without programming experience.

What is this article about?

Many people know ChatGPT: you ask a question and get an answer. But what if the AI should not only use general knowledge, but also know your own documents, such as your recipes, your manual or your notes?

That is exactly what this article shows. You will learn how to run a local AI for free on your own computer and "feed" it with your own documents, without any data leaving the house, without monthly subscription costs and without an internet connection.

Beginner's guide

Local AI with
your own data

Offline, free, private: your AI knows only your documents.

How it works

💬

Your question

›

🗂

Search documents

›

🤖

AI answers

›

🔒

Everything stays local

Local or cloud?

🖥 Local (this guide)

Free
100% offline
For beginners & private use

☁️ Cloud (Mittwald AI)

GDPR, made in Germany
No US Cloud Act
For teams & companies

From about 20 employees, we recommend a professional solution.

No obligation · No prior knowledge needed

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About the author on dr-dsgvo.de

My name is Klaus Meffert. I have a doctorate in computer science and have been working professionally and practically with information technology for over 30 years. I also work as an expert in IT & data protection. I achieve my results by looking at technology and law. This seems absolutely essential to me when it comes to digital data protection. My company, IT Logic GmbH, also offers consulting and development of optimized and secure AI solutions.