📚 ReadifyAI — Multi-PDF Question Answering App

ReadifyAI is a powerful, intuitive, and fast PDF question-answering app that allows you to upload multiple PDFs and ask intelligent questions like:

• 🔍 "Give a summary of Chapter 3"
• ❓ "Explain the concept of Quantum Entanglement"
• 📑 "What are the key takeaways from the document?"

It uses Google's Gemini model via LangChain, FAISS vector store, and Streamlit to build a conversational interface around your documents.

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🚀 Features

✅ Upload multiple PDFs
✅ Ask any question related to the documents
✅ Get detailed, accurate answers
✅ Works for summaries, explanations, definitions, and context-based answers
✅ Built with Gemini 1.5 Flash, FAISS, and LangChain

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🛠️ Installation

1. Clone the repository

git clone https://github.com/SUJALGOYALL/readifyai.git
cd readifyai

2. Create a virtual environment

# If using conda:
conda create -n readify python=3.11
conda activate readify

# OR using venv:
python -m venv venv
venv\Scripts\activate  

3. Install dependencies

pip install -r requirements.txt

4. Set up your Google API Key

Create a .env file in the root directory:

GOOGLE_API_KEY=your_google_gemini_api_key_here

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📦 Requirements

Here are the Python packages required (also present in requirements.txt):

streamlit
google-generativeai
python-dotenv
langchain
PyPDF2
faiss-cpu
langchain_google_genai

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▶️ Usage

1. Run the app:

streamlit run app.py

2. In the browser:
   • Upload one or more PDF files using the sidebar
   • Click “Submit & Process”
   • Ask any question in the input field
   • View intelligent answers based on the content of your PDFs

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🧠 How It Works

1. PDF Extraction: Extracts text from uploaded PDF(s) using PyPDF2.
2. Text Chunking: Splits the text into manageable chunks with overlap for context.
3. Embedding: Generates embeddings using GoogleGenerativeAIEmbeddings.
4. Vector Store: Stores embeddings in a FAISS index for fast similarity search.
5. Q&A Chain: Uses LangChain’s load_qa_chain and Gemini-1.5 Flash to generate answers from the retrieved documents.

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📌 Example Use Cases

• Study notes summarizer
• Research paper Q&A assistant
• Legal/contract document explainer
• Book chapter explainer
• Technical documentation assistant

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🧾 License

This project is for educational and research purposes only.

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🙋‍♂️ Author

Sujal Goyal
Mathematics & Computing, IIIT Bhagalpur

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