Mar 25, 2026 · Local AI / Intermediate · ~3 MIN READ

Use Local AI to Search Your Own Documents

Ask questions about your own documentation without uploading files to an external AI service — RAG explained in plain language.

Who This Is For

Intermediate.

Think of it like a librarian who’s actually read every book on the shelf. Ask a question and they point straight to the right paragraph, instead of you skimming every book yourself.

What You’ll Build

A local AI assistant answers a question from your own document set and identifies the source material.

Prerequisites

Ollama and Open WebUI already running (see the Local AI article)
A small test document set

Architecture

Retrieval-Augmented Generation (RAG), in plain terms: documents get processed and split into chunks, each chunk is turned into an embedding, embeddings live in a vector store, a question retrieves the most relevant chunks, and the local model answers using only that retrieved context.

Use Cases

Homelab documentation
Network diagrams and notes
PDF manuals
Personal research notes

Select an Embedding Model

Open WebUI supports several embedding models out of the box, a smaller embedding model is usually fine even if your chat model is large.

Upload a Test Document Set

Start small, five to ten representative documents, before importing an entire library.

Create Focused Collections

Network docs
Server notes
Vendor manuals
Personal research

Ask Answerable Questions

Start with questions you already know the answer to, so you can verify the retrieval is actually working correctly before trusting it.

Verify Citations

Always check the source document Open WebUI cites, treat an uncited answer with more suspicion.

Improve Results

Use clear, descriptive file names
Keep documents clean and well-formatted
Use smaller, more focused collections rather than one giant dump
Write clear, specific prompts

Security & Backup Notes

“Local” does not automatically mean “secure”, protect the Open WebUI interface itself, and back up the knowledge base like any other data

Troubleshooting

Retrieval finds irrelevant chunks, collection is too broad; split into smaller, topic-focused collections
PDF text extraction fails, scanned PDFs need OCR first (see Paperless-ngx) before they’re searchable
Context window too small, reduce the number of retrieved chunks or use a model with a larger context length
Model invents answers, a smaller/weaker model or an overly broad collection increases hallucination risk; verify citations
Document upload exposes sensitive info to other users, scope collections per-user if multiple people share the instance

Lab Finish Line

A local AI assistant answers a question from a user-supplied document and identifies the source material.