Build a Local LLM Tooling Stack with LangChain and SQLite

Create a private retrieval-augmented assistant that indexes your documents locally and answers questions with citations.

You can build a document assistant with an impressive demo in an afternoon.

Ask it a question about a PDF. Get a fluent answer. Watch it quote a paragraph from your files.

Then ask a question that your documents cannot answer.

That is where things get interesting.

A retrieval-augmented generation (RAG) system does not automatically know when its sources are insufficient. It can retrieve the wrong passage, interpret a partial match as evidence, or generate a confident answer that sounds grounded but is not. Adding a local language model does not eliminate these problems. It changes where the model runs, not how reliably it reasons about evidence.

The goal of this project is to build something more useful than a chatbot wrapped around a folder of PDFs: a private, local-first assistant that indexes documents, retrieves relevant passages, generates answers, and returns citations you can actually verify.

We will use Python, LangChain, SQLite, a local embedding model, and Ollama to build the stack.