Build an AI PDF Search Engine in a Weekend (Python, FAISS, RAG — Full Code)

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.

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Published via Towards AI