Running large language models (LLMs) is becoming increasingly accessible. With the processing power of modern Apple Silicon (M1/M2/M3) Macs, you can run powerful AI locally without latency. It only becomes truly valuable when you give the model knowledge about your specific data. This process is called Retrieval-Augmented Generation (RAG).

The privacy advantage

100% Local: When you use cloud services, you may be sending sensitive business or private documents to external servers. With a local RAG setup, all data remains on your own Mac. The models, documents, and your chat history never leave your hard drive.

How RAG works (in brief)

A base AI model doesn't know what is in your contracts, notes, or manuals. RAG solves this in two steps:

Retrieval: Your files are split up and stored as mathematical values in a vector store. When a question is asked, the system searches for the most relevant paragraphs.
Generation: These retrieved paragraphs are sent along with your question to the language model. The model then formulates an answer based on this specific context.

The required tools

Ollama: The engine to run models (like Llama 3 or Mistral) smoothly and locally on macOS.
An Embedding Model: A lightweight model (e.g., nomic-embed-text) that converts text into vectors.
Vector Database: Software like ChromaDB, Faiss, or Qdrant to search vectors at lightning speed.
Application Layer / GUI: To tie everything together without writing code, you can use tools like AnythingLLM, LM Studio, or PrivateGPT.

Step-by-step guide for macOS

Step 1: Install the LLM Engine

Download Ollama. Open your Terminal app and enter the command ollama run llama3 (or choose another model). This immediately downloads and starts the language model.

Step 2: Set up the RAG environment

For a quick start without programming, AnythingLLM Desktop is an excellent choice for Mac. Download the application and install it. Would you rather build it yourself? Learn more about the fundamentals in our section: Learning about Base AI Models.

Step 3: Connection and feeding documents

In the settings of your RAG application (such as AnythingLLM), choose Ollama as your LLM Provider, and for your Vector Database Provider, choose the built-in local option (such as LanceDB or Chroma). Create a 'Workspace' and drag your PDF or TXT files into the tool. Click "Save and Embed" to make the document accessible to the system.

Step 4: Run query

You can now ask questions in the chat window. The system first analyzes the vector database for relevant paragraphs and invisibly sends them along to Ollama. The result is an accurate answer, generated completely offline.