ChefMind AI
Intelligent Kitchen Recommendation System
A full-stack AI application I built from scratch, combining vector embeddings, semantic search, and hybrid ranking algorithms to solve the daily "what's for dinner?" problem.
The Insight
Have you ever noticed something fascinating? Same kitchen. Same ingredients. Yet one person commits an atrocity against the human tongue while another pulls together a gourmet meal that brings the whole family to the table.
The difference isn't the ingredients, it's knowing what to do with them.
"The difference is knowing what to do with them."
The Problem
Every evening, millions of people open their fridge and ask: "What's for dinner?" They have tomatoes, chicken, half an onion, and mysterious leftovers. The meal is in there somewhere, but the mental load of figuring it out turns cooking from joy into a chore.
The Solution
Upload your kitchen inventory. Get AI-powered recipe recommendations ranked by what you actually have, what you like, and what fits your dietary needs.
About the Developer
I designed and built this entire system, from the embedding pipeline to the mobile app. This project showcases my ability to work across the full AI application stack.
What I Built:
- Vector embedding pipeline with OpenAI's text-embedding-3-small
- Production vector database migration (ChromaDB → Pinecone)
- RESTful API with JWT authentication and secure password hashing
- Hybrid scoring algorithm balancing relevance + availability
- Cross-platform delivery: CLI, React Native mobile, React PWA
- Serverless deployment on Netlify Functions
App Demo
Experience the application directly below. Try interacting with the interface to see how ChefMind AI helps manage your kitchen and suggests recipes.
How It Works
Ingredients go in, ranked recipe matches come out. Follow the engine from your kitchen inventory to the final ranking.
- 01
Kitchen Inventory
You upload what is actually in your kitchen. Chicken, tomatoes, rice, garlic, and whatever else is on hand becomes the starting point.
Ingredients in
- 02
Semantic Embeddings
Each ingredient is converted into a vector with OpenAI text-embedding-3-small. Unlike keyword matching, the AI understands that chicken breast relates to poultry dishes and that tomatoes work in both Italian and Mexican cuisine.
Meaning, not keywords
- 03
Vector Search
Pinecone searches the recipe space for the closest matches to your ingredient vectors, surfacing dishes you can realistically make.
Closest recipes surface
- 04
Smart Ranking
A hybrid scoring algorithm ranks the results, weighting semantic relevance at 70 percent and availability at 30 percent, so the top suggestions are both a good fit and actually cookable right now.
Ranked by fit and availability
Recipe Match Engine
From ingredients to ranked matches
Input
chicken, tomatoes, rice, garlic, soy sauce, eggs, spinach, coconut milk...
Output
Chicken Stir Fry, 94% Match, Ready to cook!
Coconut Curry Rice, 91% Match, Ready to cook!
Egg Fried Rice, 85% Match, Missing: green onions
Hybrid Scoring Algorithm
Full Feature Set
AI Recipe Matching
Semantic search finds recipes that fit your ingredients
Inventory Management
Track quantities, auto-deduct when you cook
Shopping List Generation
Auto-generate lists based on low stock & recipes
User Authentication
Secure accounts with JWT & encrypted passwords
Dietary Preferences
Filter by allergies, restrictions, favorite cuisines
Recipe Images
Beautiful food photography via AWS S3
Serverless API
Production-ready deployment on Netlify
Cross-Platform
CLI, Mobile, Web, and API interfaces
Technical Implementation
| Component | Technology |
|---|---|
| Vector Database | Pinecone (production-scale) |
| Embeddings | OpenAI text-embedding-3-small |
| Backend API | Node.js + Express |
| Authentication | JWT + bcrypt |
| Cloud Storage | AWS S3 |
| Deployment | Netlify Functions (serverless) |
| Mobile | React Native + Expo |
| Web | React PWA + Material-UI |
API Architecture
POST /api/auth/signup → Create accountPOST /api/auth/login → JWT authenticationGET /api/inventory → View kitchen stockPOST /api/inventory/restock → Add items after shoppingPOST /api/recommendations → Get AI-matched recipesPOST /api/recipes/accept → Cook & auto-deduct ingredientsPOST /api/shopping-list/generate → Smart shopping listPerformance
Recipes
Food Groups
Ingredients
Query Response
Accuracy
Platforms
Skills Demonstrated
Backend & API
- • RESTful API design (Express.js)
- • JWT authentication & middleware
- • Secure password hashing (bcrypt)
AI/ML Engineering
- • Vector embeddings & semantic search
- • Pinecone vector database
- • OpenAI API integration
- • Hybrid ranking algorithms
Cloud Infrastructure
- • AWS S3 for asset storage
- • Serverless deployment (Netlify)
- • Environment-based configuration
Full-Stack Delivery
- • React Native mobile app
- • Progressive Web App
- • Cross-platform architecture