Redis Semantic Cache Demo

A comprehensive web application demonstrating semantic caching capabilities using Redis Cloud and OpenAI, replicating and enhancing the functionality of the original demo_search.py script.

🚀 Features

• Interactive Web Interface: Beautiful, responsive UI built with Bootstrap and Font Awesome
• Single Query Testing: Execute individual queries and observe caching behavior
• Semantic Caching Demo: Run identical queries twice to demonstrate cache performance
• Similarity Demo: Test semantically similar queries to showcase cache effectiveness
• Real-time Performance Metrics: View query timing, cache hit/miss status, and speedup calculations
• Query History: Track all executed queries with timestamps and performance data
• API Status Monitoring: Real-time OpenAI API connectivity and quota status checking
• Accordion Interface: Clean, collapsible response display with proper formatting

📋 Prerequisites

• Python 3.8+
• Redis Cloud account and connection string
• OpenAI API key
• Virtual environment (recommended)

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/svkmsr6/redis-semantic-cache-demo.git
   cd redis-semantic-cache-demo

2. Create and activate virtual environment:

   python -m venv redis-searchenv
   # On Windows:
   redis-searchenv\Scripts\activate
   # On macOS/Linux:
   source redis-searchenv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables: Create a .env file in the root directory:

   RDS_URI=your_redis_connection_string
   OPENAI_API_KEY=your_openai_api_key

🚀 Quick Start

Web Application

python app.py

Then open your browser to: http://localhost:5000

Command Line Demo

python demo_search.py

Convenience Scripts

• Windows: Double-click start_webapp.bat or run start_webapp.ps1
• Cross-platform: Use the VS Code task "Run Redis Semantic Cache Web App"

📱 Web Interface

The web application provides three main demonstration modes:

1. Single Query

• Enter any query to test individual caching behavior
• See real-time performance metrics
• View cache hit/miss status

2. Caching Demo

• Executes the same query twice
• Demonstrates cache performance improvement
• Shows detailed performance analysis with speedup calculations

3. Similarity Demo

• Tests semantically similar queries
• Choose from default queries or create custom ones
• Observe how semantic similarity affects caching

🏗️ Project Structure

redis-semantic-cache/
├── app.py                      # Flask web application
├── demo_search.py             # Original command-line demo
├── rsearch_module/            # Core semantic search functionality
│   └── __init__.py
├── templates/
│   └── index.html            # Web interface template
├── static/
│   └── style.css            # Custom styling
├── requirements.txt          # Python dependencies
├── start_webapp.bat         # Windows launcher
├── start_webapp.ps1         # PowerShell launcher
├── README_webapp.md         # Web app specific documentation
└── .env                     # Environment variables (create this)

🔧 API Endpoints

The Flask application exposes several REST endpoints:

• GET / - Main web interface
• GET /api/status - Check OpenAI API status
• POST /api/query - Execute a single query
• POST /api/demo/caching - Run caching demonstration
• POST /api/demo/similarity - Run similarity demonstration
• GET /api/history - Get query history
• POST /api/history/clear - Clear query history

📊 Performance Features

The application tracks and displays:

• Query Execution Time: Precise timing for each query
• Cache Hit/Miss Status: Visual indicators for cache effectiveness
• Performance Speedup: Calculated improvement from caching
• Time Saved: Actual time savings from cache hits

🎨 User Interface

• Responsive Design: Works on desktop, tablet, and mobile
• Accordion Responses: Clean, collapsible display of AI responses
• Real-time Status: Live API connectivity monitoring
• Performance Metrics: Visual charts and indicators
• Query History: Persistent session history with timestamps

🔍 Core Technologies

• Backend: Flask, OpenAI API, Redis
• Frontend: Bootstrap 5, Font Awesome, Vanilla JavaScript
• Caching: Redis Vector Library (redisvl) with semantic cache
• Embeddings: Sentence Transformers (all-mpnet-base-v2)
• AI Model: OpenAI GPT-4o-mini

🚨 Troubleshooting

Common Issues

1. API Status Red:

   • Check your OpenAI API key in .env
   • Verify billing status at OpenAI dashboard

2. No Cache Hits:

   • Verify Redis connection string
   • Check Redis Cloud connectivity

3. Slow Initial Queries:

   • Normal behavior for first-time queries
   • Subsequent similar queries should be faster

4. Module Import Errors:

   • Ensure virtual environment is activated
   • Run pip install -r requirements.txt

Error Messages

• QUOTA_EXCEEDED: Check OpenAI billing and usage limits
• AUTH_ERROR: Verify OpenAI API key is correct
• RATE_LIMITED: Wait and retry, or upgrade OpenAI plan

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add 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 - see the LICENSE file for details.

🙏 Acknowledgments

• Redis for powerful caching capabilities
• OpenAI for advanced language models
• Bootstrap team for excellent UI framework
• Sentence Transformers for embedding models

📞 Support

If you encounter any issues or have questions:

1. Check the troubleshooting section above
2. Review the issues page
3. Create a new issue with detailed information

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Made with ❤️ for the Redis and AI community