3.4 KiB
🎩 Tuxedo Link
AI-Powered Cat Adoption Search Engine
Find your perfect feline companion using AI, semantic search, and multi-platform aggregation.
In loving memory of Kyra 🐱
🌟 Overview
Tuxedo Link is an intelligent cat adoption platform that combines:
- Natural Language Understanding - Describe your ideal cat in plain English
- Semantic Search with RAG - ChromaDB + SentenceTransformers for personality-based matching
- Multi-Modal Deduplication - Uses CLIP for image similarity + text analysis
- Hybrid Scoring - 60% vector similarity + 40% attribute matching
- Multi-Platform Aggregation - Searches Petfinder and RescueGroups APIs
- Serverless Architecture - Optional Modal deployment with scheduled email alerts
Tech Stack: OpenAI GPT-4 • ChromaDB • CLIP • Gradio • Modal
📸 Application Screenshots
🔍 Search Interface
Natural language search with semantic matching and personality-based results:
🔔 Email Alerts
Save your search and get notified when new matching cats are available:
📖 About Page
Learn about the technology and inspiration behind Tuxedo Link:
📧 Email Notifications
Receive beautiful email alerts with your perfect matches:
🚀 Full Project & Source Code
The complete source code, documentation, and setup instructions are available at:
👉 GitHub Repository: dkisselev-zz/tuxedo-link
The repository includes:
- ✅ Complete source code with 92 passing tests
- ✅ Comprehensive technical documentation (3,400+ lines)
- ✅ Agentic architecture with 7 specialized agents
- ✅ Dual vector store implementation (main + metadata)
- ✅ Modal deployment guide for production
- ✅ Setup scripts and configuration examples
- ✅ LLM techniques documentation (structured output, RAG, hybrid search)
🧠 Key LLM/RAG Techniques
1. Structured Output with GPT-4 Function Calling
Extracts search preferences from natural language into Pydantic models
2. Dual Vector Store Architecture
- Main ChromaDB - Cat profile semantic embeddings
- Metadata DB - Fuzzy color/breed matching with typo tolerance
3. Hybrid Search Strategy
Combines vector similarity (60%) with structured metadata filtering (40%)
4. 3-Tier Semantic Normalization
Dictionary → Vector DB → Fuzzy fallback for robust term mapping
5. Multi-Modal Deduplication
Fingerprint + text (Levenshtein) + image (CLIP) similarity scoring
🏆 Project Highlights
- 92 Tests - 81 unit + 11 integration tests (100% passing)
- Production Ready - Serverless Modal deployment with volumes
- Email Alerts - Scheduled background jobs for new match notifications
- 95%+ Accuracy - Multi-modal deduplication across platforms
- 85-90% Match Quality - Hybrid scoring algorithm
📚 Documentation
- TECHNICAL_REFERENCE.md - Complete API documentation
- MODAL_DEPLOYMENT.md - Cloud deployment guide
- ARCHITECTURE_DIAGRAM.md - System architecture visuals
- tests/README.md - Testing guide and coverage
Made with ❤️ in memory of Kyra
May every cat find their perfect home 🐾