Build a document Q&A app

After this lesson you can: Ship a working app that answers questions about your own PDFs.

First: Vector databases and similarity search, Call an LLM API from Python

Watch

• How to Build a Production-Ready RAG AI Agent in Python (Step-by-Step) - Tech With Tim, 76 min. A substantial Python walkthrough likely fits building and packaging a practical RAG document Q&A app.
• How to Build a Local AI Agent With Python (Ollama, LangChain & RAG) - Tech With Tim, 28 min. A shorter local RAG walkthrough aligns with no paid API credit and approachable implementation constraints.

Notes

A document Q&A app is just a small pipeline wrapped in a user interface. You load PDFs, split their text into chunks, turn each chunk into an embedding, store those embeddings in a vector index, and at question time retrieve the most relevant chunks before asking a language model to answer using that context. The model is not magically reading your whole document; your code is selecting the few pieces it should see.

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