Privacy-First RAG: Chatting with Your Local Document Repositories Safely

Retrieval-Augmented Generation (RAG) is the gold standard for talking to your documentation. It queries files, extracts the most relevant segments, and passes them to the LLM as reference context. But when you use cloud solutions, you upload financial reports, medical files, or legal records to a third party.

In this tutorial, we will set up a 100% private RAG system. Your documents remain encrypted on your hard drive, analyzed by a local embedding model, and answered by a local LLM.

The Architecture of Private RAG

A RAG pipeline consists of three core components, all of which can run locally:

1. Document Parser & Vector Database: Splices PDFs or text files into digestible chunks and indexes them using mathematical embeddings (using ChromaDB or LanceDB).
2. Embedding Model: A specialized, small model (like nomic-embed-text) that converts text passages into vectors.
3. Inference LLM: A conversational model (like mistral) that reads the retrieved context and answers your query.

Step-by-Step Setup using Open WebUI

The easiest way to orchestrate local RAG is through Open WebUI. It features built-in document ingestion, vector stores, and seamless linking to Ollama.

1. Pull the Embedding Model

Open your command line and retrieve the Nomic embedding model. It runs extremely fast on CPU or GPU cores:

ollama pull nomic-embed-text

2. Deploy Open WebUI via Docker

Running Open WebUI via Docker ensures all configurations, vector databases, and cache folders are self-contained. Run the following command:

docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main

3. Upload and Query Documents

Open your browser and navigate to http://localhost:3000. In the chat interface:

• Click the "+" button or drag and drop your confidential PDFs, CSVs, or text files into the window.
• Open WebUI will parse the file, generate text embeddings using nomic-embed-text, and index them in its internal Chroma vector database.
• Type # followed by the file name in the prompt field. Open WebUI will now retrieve relevant context passages and pass them directly to your active local LLM (e.g., Llama 3.1).