Build a Private RAG System on Your Local Machine with LEANN

Ever wanted to run a Retrieval-Augmented Generation (RAG) system but hesitated because of API costs, data privacy concerns, or just the sheer complexity of the setup? What if you could have a powerful RAG pipeline running entirely on your own machine, using open-source models and your own documents? That’s exactly what the LEANN project enables.

It’s a local-first RAG system that strips away the cloud dependencies and puts you in full control. No data leaves your computer, and you’re not locked into any service. For developers prototyping ideas, working with sensitive data, or just wanting to understand RAG internals, this is a compelling playground.

What It Does

LEANN is a local RAG (Retrieval-Augmented Generation) system. In simple terms, it lets you upload your own documents (like PDFs, text files, or markdown), automatically processes and indexes them, and then lets you ask questions in natural language. The system finds relevant information from your documents and uses a local language model to generate an answer based on that context.

The core workflow is classic RAG: ingest, embed, retrieve, generate. But the key is that every step happens on your local hardware.

Why It’s Cool

The main appeal here is the privacy and control. Since everything runs locally, you can use it for internal company docs, personal notes, or any sensitive material without a second thought. It’s also a fantastic learning tool. You can peek under the hood, see how the chunks are created, how the vector search works, and how the prompt is assembled for the LLM.

It’s built with a pragmatic stack—likely using something like Sentence Transformers for embeddings, a local vector store (maybe FAISS or Chroma), and a quantized model from the Hugging Face ecosystem run via Ollama or similar. This keeps it lightweight and manageable on a modern laptop.

Another cool aspect is the self-contained nature. You’re not wiring together five different cloud services. It’s a single project you clone, set up, and run. This dramatically lowers the barrier to entry for developers wanting to experiment with RAG concepts hands-on.

How to Try It

Getting started is straightforward. Head over to the GitHub repository to clone the project and check the prerequisites.

git clone https://github.com/yichuan-w/LEANN
cd LEANN

Follow the setup instructions in the README.md. You’ll likely need Python, pip, and probably Ollama installed to run the local LLM. After installing the dependencies and setting up any required environment variables, you can usually run a single command to launch the application, like python app.py or using a provided script.

The interface is typically a simple local web UI. From there, you can upload your documents and start asking questions.

Final Thoughts

LEANN is a great example of the growing trend towards powerful, local AI tooling. It won’t match the scale or speed of a cloud GPT-4 setup, but for many personal or internal use cases, it’s more than sufficient. The trade-off for total privacy and zero cost is absolutely worth it for the right project.

As a developer, this is a perfect sandbox. Use it to prototype a document Q&A feature, to understand RAG mechanics better, or to build a completely private knowledge assistant. The fact that it runs on your machine means you can hack on it, modify the chunking logic, try different embedding models, or swap out the LLM—all without asking for permission or paying a bill.

Give it a spin if you’ve been curious about RAG. There’s no better way to learn than by running the whole pipeline on your own terms.

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