Search Agent - AI-Powered Web Search Assistant

A modern, responsive AI-powered search application that combines web search capabilities with conversational AI to provide comprehensive answers to user queries.

✨ Features

• 🔍 Intelligent Web Search: Uses Tavily API for real-time web search
• 🤖 AI-Powered Responses: Powered by Google Gemini 2.0 Flash model
• 💬 Real-time Streaming: Server-sent events for live response streaming
• 🎯 Search Progress Tracking: Visual indicators for search stages
• 📋 Copy Functionality: Easy copy-to-clipboard for AI responses
• 🔄 Session Management: Start fresh conversations anytime
• 🎨 Session Context: Custom prompt engineering for personalized AI responses
• 💡 Interactive UI: Modern design with smooth animations

📸 Screenshots

Main Application Interface

The main chat interface showing real-time AI responses with web search integration

Custom Session Context

Session Context feature allowing users to customize AI behavior with predefined presets or custom prompts

🏗️ Architecture

Backend (Python/FastAPI)

• FastAPI: Modern web framework for building APIs
• LangGraph: State management for AI workflows
• LangChain: Integration with various AI models and tools
• Tavily: Web search API for real-time information retrieval
• Google Gemini: Advanced language model for responses

Frontend (Next.js/React)

• Next.js 15: React framework with TypeScript
• Tailwind CSS: Utility-first CSS framework for responsive design
• Server-Sent Events: Real-time communication with backend
• TypeScript: Type-safe development

🚀 Setup Instructions

Prerequisites

• Python 3.8+
• Node.js 18+
• API Keys for:
  • Google Gemini API
  • Tavily Search API

Backend Setup

1. Navigate to the server directory:

   cd server

2. Create and activate a virtual environment:

   # Windows
   python -m venv venv
   venv\Scripts\activate
   
   # macOS/Linux
   python -m venv venv
   source venv/bin/activate

   python -m venv venv
   venv\Scripts\activate  # Windows
   # or
   source venv/bin/activate  # Linux/Mac

3. Install dependencies:

   pip install -r requirements.txt

4. Configure environment variables in .env:

   GOOGLE_API_KEY=your_google_api_key_here
   TAVILY_API_KEY=your_tavily_api_key_here
   
   # Optional: LangSmith for debugging
   LANGSMITH_TRACING=true
   LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
   LANGSMITH_API_KEY=your_langsmith_key
   LANGSMITH_PROJECT=your_project_name

5. Start the backend server:

   uvicorn app:app --reload --host 0.0.0.0 --port 8000

Frontend Setup

1. Navigate to the client directory:

   cd client

2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

The application will be available at:

• Frontend: http://localhost:3000 (or next available port)
• Backend: http://localhost:8000
• Backend Health Check: http://localhost:8000/health

Usage

1. Open the frontend application in your browser
2. Type your question in the input field
3. Watch as the AI searches the web and compiles a response
4. Copy responses using the copy button

Navigation

• Home Tab: Welcome page with project overview and "Perplexity 2.0" branding
• Chat Tab: Main conversational interface with AI search capabilities
• Settings Tab: Customize your experience (theme, search preferences, etc.)

Session Context Feature

1. Click the large Session Context button (bottom-right corner)
2. Choose from Quick Presets:
   • General Assistant
   • Technical Expert
   • Research Assistant
   • Simple Explainer
3. Or write your own Custom Context to guide AI responses
4. The context applies to all messages in the current session

Chat Features

1. Ask Questions: Type your question in the input field
2. Real-time Responses: Watch as the AI searches the web and streams responses
3. Search Progress: Visual indicators show search stages (searching → reading → writing)
4. Copy Responses: Click the copy button on any AI response

Tips for Better Results

• Use the Session Context to specify your role or expertise level
• Ask specific questions for more targeted search results
• Try different contexts for the same question to get varied perspectives

Development

Adding New Features

1. Backend: Add new endpoints in app.py or extend the LangGraph workflow
2. Frontend: Add new components in src/components/ or extend existing functionality

Environment Variables

The application uses several environment variables:

• GOOGLE_API_KEY: Required for AI responses
• TAVILY_API_KEY: Required for web search
• LANGSMITH_*: Optional for debugging and monitoring

Performance Tips

• Use specific, focused queries for better search results
• Set appropriate session context to reduce token usage
• Monitor API usage in Google AI Studio and Tavily dashboards

Building for Production

1. Backend Deployment:

   # Install production dependencies
   pip install gunicorn
   
   # Run with gunicorn
   gunicorn app:app -w 4 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000

2. Frontend Build:

   cd client
   npm run build
   npm start

3. Environment Variables: Set production API keys and endpoints

4. Security: Configure CORS, rate limiting, and authentication as needed

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

This project is for educational and development purposes.