RAG Chatbot: Chat with Your PDFs

Overview

RAG Chatbot - A lightweight application to upload PDF files and ask natural-language questions grounded in document content.

What Users Can Do

• File Upload: Upload one or multiple PDF files and process them into searchable chunks.
• Question Answering: Ask questions and receive answers generated from the most relevant document context.
• Conversation Continuation: Continue conversations with reply-style follow-ups while keeping context in session history.

Why I built this

I built this platform to solve a fundamental issue I faced while studying as follows:

• Reading long PDFs manually is slow and makes information retrieval inefficient.
• It is difficult to quickly find precise answers across multiple documents during revision.

Tech Stack

Python Streamlit OpenRouter API scikit-learn PyPDF2 NumPy python-dotenv

After launch & Impact

• Made document Q&A faster and easier for personal study and quick reference.
• Deployed a working public app on Streamlit Cloud for easy access and sharing.
• Improved understanding of end-to-end RAG flow from ingestion to retrieval to generation.
• Built practical experience in model integration, rate limiting, and user-session handling.

Future Plans

• Improve document chunking and retrieval quality for more accurate answers.
• Add support for more file formats and richer citation-style responses.
• Introduce configurable model and retrieval settings for advanced users.