pdf summarizer

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  google colab screenshot

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  importing library and testing

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  github repo & code screenshot

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 I have face

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

Built With

• fastapi
• gemini
• huggingface
• langchain
• python