Build an AI PDF Search Engine in a Weekend (Python, FAISS, RAG — Full Code)
Last Updated on September 4, 2025 by Editorial Team
Author(s): Tarun Singh
Originally published on Towards AI.
Turn messy folders of PDFs into a blazing-fast, AI-assisted knowledge base you can actually talk to.
I had a problem: dozens (okay, hundreds) of PDFs — research papers, API docs, whitepapers — scattered across folders. Search was slow, skimming was worse. So I built a PDF Q&A engine that ingests, chunks, embeds, indexes with FAISS, retrieves the best passages, and drafts a concise answer (with a no-API fallback). Here’s everything — end-to-end code, explained in plain English.
This article explains how to create a local PDF Q&A engine in Python using tools like FAISS for indexing and retrieving document passages, and Gradio for building a web interface. It covers project structure, essential Python files needed for loading PDFs, chunking documents for context, embedding them for similarity search, storing them in databases, and generating answers using an optional LLM. The author shares key learnings, next upgrade ideas, and helpful links to relevant resources.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
