How to Chat With Your Own Documents Without Uploading Them (Local RAG Explained)
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How we wrote this: An explainer based on official documentation from Ollama, Open WebUI and Hugging Face (linked below). We have not tested these tools against each other. Software features change quickly; check the docs for your version.
Asking an AI about your own contracts, notes or manuals is one of the most useful things it can do. It's also the use case where people most want to avoid uploading files to a cloud service. The technique that makes it work is called retrieval-augmented generation (RAG), and it can run entirely on your own machine.
RAG in plain English
A language model doesn't "know" your documents. RAG works around that in three steps:
- Index. Your documents are split into chunks, and each chunk is turned into a list of numbers called an embedding that captures its meaning. Embeddings are stored in a searchable index.
- Retrieve. When you ask a question, the question is embedded too, and the most similar chunks are pulled from the index.
- Generate. Those chunks are pasted into the model's prompt with your question, so the model answers from your text.
Each step can happen locally. Ollama's command reference includes running an embedding model (its example is embeddinggemma) alongside chat models.
A common local setup
- Model runner: Ollama runs the chat and embedding models on your computer. New to it? Start with our Ollama beginner walkthrough.
- Interface: Open WebUI describes itself as a self-hosted AI platform "built to run entirely offline," with support for Ollama and OpenAI-compatible APIs. Its documentation lists RAG among its core features.
Other tools work in a similar way. Whatever you pick, the questions below are the ones that matter.
Privacy checks before you load real documents
- Is the model local? Some tools can switch quietly between local and cloud models. Ollama's FAQ says it doesn't see prompts or data when you run locally, and explains how to disable its cloud features (
OLLAMA_NO_CLOUD=1). - Is the server exposed? Ollama listens only on
127.0.0.1by default. Keep it that way unless you deliberately need network access. - Where is the index stored? Your embeddings and chunks are a copy of your documents. Back them up and protect them like the originals.
- Are any web or search features on? Web search sends queries out. Turn it off for confidential work.
For a fuller definition of what "private" means, see What "private AI" really means on Private AI Computer.
What affects answer quality
- Context length. Retrieved chunks have to fit in the model's context window. Ollama's context-length docs note that tasks needing large context should be set to at least 64,000 tokens, and that larger contexts need more memory. See context length and the KV cache.
- Model size and quantization. Bigger models usually reason better over retrieved text. Quantized formats like GGUF make them fit. Use the Private AI Computer calculator to see what your hardware can run.
- Document hygiene. Scanned PDFs without a text layer, duplicate files and outdated versions all reduce accuracy. Clean the folder before indexing.
Limits to keep in mind
RAG reduces made-up answers but doesn't eliminate them. Ask the tool to cite the passages it used, and check those passages for anything important, especially legal, medical or financial questions.