Apify blog post walks through building a PDF question-answering pipeline with Python and RAG

Apify published a tutorial on constructing a Python pipeline that uses retrieval-augmented generation to answer questions from PDFs and cite the source page.

Markdown twin JSON

Extraction & Parsing Primary source analysis / significance 2

Briefing

Apify: Apify published a tutorial on constructing a Python pipeline that uses retrieval-augmented generation to answer questions from PDFs and cite the source page.

Why it matters

This tutorial reflects the growing trend of combining extraction (PDF parsing) with RAG for grounded, verifiable answers. By emphasizing citation checking, it highlights a key challenge in AI-assisted data extraction: trustworthiness of retrieved content. Practitioners can use this as a template for building auditable QA systems over documents.

Sources

Primary source · Apify · 2026-10-14 — How to do question answering from a PDF with Python and RAG

Watch next

How will Apify's tooling evolve to support real-time citation verification in RAG pipelines?

Topics: Apify, pdf-extraction, rag, question-answering, python, citations, tutorial