Perplexity 2.0

A modern, responsive AI chat interface with integrated web search functionality. Perplexity 2.0 provides a clean UI similar to Perplexity.ai, combining conversational AI with real-time search capabilities.

✨ Features

• Real-time AI Responses – Stream AI responses as they're generated
• Integrated Web Search – AI can search the web for up-to-date information
• Conversation Memory – Maintains context throughout your conversation
• Search Process Transparency – Visual indicators show searching, reading, and writing stages
• Responsive Design – Clean, modern UI that works across devices

---

🏗️ Architecture

Perplexity 2.0 follows a client-server architecture:

Client (Next.js + React)

• Modern React application built with Next.js
• Real-time streaming updates using Server-Sent Events (SSE)
• Components for message display, search status, and input handling

Server (FastAPI + LangGraph)

• Python backend using FastAPI for API endpoints
• LangGraph implementation for conversation flow with LLM and tools
• Integration with Tavily Search API for web searching capabilities
• Groq LLM (LLaMA 3) for fast and cost-free AI inference
• Server-Sent Events for real-time streaming of AI responses

---

🚀 Getting Started

Prerequisites

• Node.js 18+
• Python 3.10+
• Groq API key
• Tavily API key

---

Installation

1. Clone the repository

git clone https://github.com/aliaa039/perplexity.git
cd perplexity

---

2. Set up the server

cd server
python -m venv .venv
source .venv/Scripts/activate   # Windows (Git Bash)
pip install -r requirements.txt

---

3. Configure environment variables

Create a .env file inside the server directory:

GROQ_API_KEY=your_groq_api_key_here
TAVILY_API_KEY=your_tavily_api_key_here

4. Set up the client

cd ../client
npm install

---

▶️ Running the Application

1. Start the server

cd server
uvicorn app:app --reload

2. Start the client

cd client
npm run dev

3. Open your browser

http://localhost:3000

---

🔍 How It Works

1. User sends a message through the chat interface

2. Server processes the message using Groq LLaMA-3

3. AI decides whether to use web search or respond directly

4. If search is needed:

   • Query is sent to Tavily Search API
   • Results are processed and injected into the prompt
   • AI formulates a grounded response

5. Response is streamed back to the client in real-time

6. Search stages are displayed to the user (searching, reading, writing)

---

🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

---

📝 License

This project is licensed under the MIT License.

---

🙏 Acknowledgments

• Inspired by the UI and functionality of Perplexity.ai
• Built with Next.js, React, FastAPI, and LangGraph
• Powered by Groq (LLaMA 3) and Tavily Search API