WikiSummarizer

AI-powered Wikipedia summarization tool that transforms articles into concise, digestible summaries using Retrieval-Augmented Generation (RAG).

Key Features

• Smart Summarization: Generate multi-section summaries (Overview, History, Controversies, Impact, etc.)
• Interactive Q&A: Get precise, context-aware answers about any topic
• Responsive Design: Optimized for both mobile and desktop
• Advanced Backend: Built with FastAPI, LangChain, and ChromaDB

Tech Stack

• Backend: FastAPI, LangChain, ChromaDB, Anthropic Claude
• Frontend: React 18, TypeScript, Tailwind CSS, Vite
• AI/ML: RAG Architecture, Vector Search, Semantic Similarity

Quick Start

Prerequisites

• Python 3.8+, Node.js 16+, Git

Installation

# Clone the repository
git clone https://github.com/jaibhasin/WikiSummarizer.git
cd WikiSummarizer

# Set up backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Set up frontend
cd frontend
npm install

Configuration

1. Create .env file in the root directory:

   ANTHROPIC_API_KEY=your_anthropic_api_key_here
   OPENAI_API_KEY=your_openai_api_key_here  # Optional for better performance
   CHROMA_PERSIST_DIRECTORY=chroma_db

Running the Application

# Start backend (from project root)
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

# In a new terminal, start frontend (from frontend directory)
npm run dev

Visit http://localhost:5173 to start using WikiSummarizer!

Usage

1. Search for a Topic: Enter any Wikipedia topic or URL
2. View Summaries: Get concise summaries in different sections
3. Ask Questions: Use the Q&A feature for specific information

Performance Tips

For faster performance, use ChatGPT embeddings by:

1. Adding your OpenAI API key to .env
2. Updating app/services/rag.py to use OpenAI embeddings

Contributing

Contributions are welcome! Please see our Contributing Guide for details.

License

MIT License - see LICENSE for details.

Support

• Issues: GitHub Issues
• Email: bhasinjai@gmail.com

Roadmap

• ChatGPT Embeddings Integration
• Multi-language Support
• Export Summaries (PDF/Markdown)
• User Accounts & History

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