PDF AI Assistant using Gemini + FAISS

AI-powered PDF assistant that answers questions directly from documents using RAG to retrieve relevant content and generate accurate, context-aware responses.

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Inspiration

With the increasing use of PDFs for research, reports, and documentation, finding specific information quickly becomes difficult and time-consuming. I wanted to build a system that allows users to interact with PDFs in a smarter way—by simply asking questions and getting direct answers.

What it does

This project is an AI-powered PDF assistant that enables users to ask questions and receive accurate answers directly from PDF documents. It uses a RAG approach to fetch relevant content and generate context-aware responses.

How I built it

Extracted PDF content using PyPDFLoader Cleaned and preprocessed text using NLTK and regex Split text into chunks using RecursiveCharacterTextSplitter Generated embeddings using Hugging Face (MiniLM model) Stored embeddings in FAISS vector database Built backend API using FastAPI Integrated Gemini API to generate answers based on retrieved context

Challenges we ran into

Handling noisy and unstructured PDF text Choosing optimal chunk size for better retrieval Debugging embedding and dependency issues Integrating multiple tools (FAISS, Gemini, FastAPI) smoothly

Accomplishments that I're proud of

Successfully built a complete RAG-based system Achieved accurate question-answering from PDFs Built a scalable backend using FastAPI

What we learned

Working with vector databases like FAISS Integrating LLMs (Gemini) with backend systems

What's next for PDF AI Assistant using Gemini + FAISS

Improve response accuracy with better prompt engineering

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