Receipt Sense 🧾

An intelligent receipt analysis application that leverages Redis 8 and Redis Stack to provide advanced price comparison, vector-based item similarity search, and real-time analytics across different stores.

🌐 Live Demo

Project is live here: receiptsense.vercel.app

🚀 Project Overview

This Next.js application transforms receipt data into actionable insights using cutting-edge database technologies. By combining OCR processing with Redis Stack's advanced modules, it delivers:

• Smart Price Analysis: Compare prices across stores with statistical insights
• Vector Similarity Search: Find similar items using AI-powered embeddings
• Real-time Recommendations: Get instant savings suggestions
• Trend Analysis: Track price movements over time
• Store Performance: Analyze which stores offer the best deals

🛠️ Tech Stack

• Frontend: Next.js 15, React 19, Tailwind CSS 4
• Backend: Next.js API Routes with TypeScript
• Database: Redis Cloud 8.0 with Redis Stack
• OCR Engine: Tesseract.js for receipt text extraction
• Deployment: Vercel with serverless functions
• Code Quality: Husky, ESLint, TypeScript

🏗️ Architecture

Data Flow

1. Receipt Upload → OCR Processing (Tesseract.js)
2. Item Extraction → Vector Embedding Generation
3. Redis Storage → JSON documents with vector indices
4. Similarity Search → KNN search using cosine distance
5. Price Analysis → Statistical comparison across stores
6. Recommendations → Real-time insights and savings suggestions

🚀 Getting Started

Prerequisites

• Node.js 18.18+ or 20+
• Redis Cloud account with Redis Stack enabled

Installation

git clone <repository-url>
npm install

Environment Setup

# Redis Cloud with Redis Stack
REDIS_URL=redis://default:password@redis-xxxxx.c91.us-east-1-3.ec2.redns.redis-cloud.com:port
NODE_ENV=development

Development

npm run dev    # Start development server
npm run lint   # Run ESLint
npm run build  # Build for production

📊 Features Deep Dive

Vector Similarity Search

• 128-dimensional embeddings for item names
• Cosine similarity for finding related products
• KNN search with configurable result limits
• Automatic filtering of zero-price items

Price Analysis Engine

• Store comparison with statistical insights
• Price volatility detection (>30% variation alerts)
• Savings recommendations based on historical data
• Trend analysis using time series data

Smart Recommendations

• Overpaid alerts (>15% above average)
• Good deal detection (>10% below average)
• Store recommendations based on price performance
• Alternative product suggestions with high similarity scores

🚀 Deployment

Vercel (Recommended)

1. Connect repository to Vercel
2. Configure environment variables
3. Deploy with automatic Redis Stack detection

Environment Variables

• REDIS_URL: Redis Cloud connection string with Redis Stack
• NODE_ENV: Production environment flag

📈 Performance Optimizations

• Vector search caching for frequently queried items
• Connection pooling with Redis client reuse
• Graceful fallbacks when Redis Stack unavailable
• Efficient embedding generation with normalized vectors
• Smart filtering to exclude invalid price data

🔍 API Endpoints

Endpoint	Method	Description
/api/upload-receipt	POST	Upload and OCR process receipts
/api/receipts	GET	Retrieve all stored receipts
/api/receipts/[id]	GET	Get specific receipt details
/api/receipts/[id]/edit	PUT	Update receipt information
/api/price-comparison/[itemName]	GET	Vector search and price analysis

🤝 Contributing

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

📄 License

MIT License - see LICENSE file for details

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Built with ❤️ using Redis 8, Redis Stack, and Next.js