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Changelog

All notable changes to the AI Fashion Assistant project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[2.5.0] - 2026-01-17

Added - Full-Stack Application & User Study

  • Full-stack web application with React 18, FastAPI, and MongoDB
  • User authentication with JWT tokens
  • User management system (profiles, preferences, history)
  • AI chat assistant with Llama-3.3-70B via GROQ API
  • Favorites system for saving preferred products
  • Search history tracking for analytics
  • User study with 25 participants
    • System Usability Scale (SUS) evaluation
    • Custom metrics evaluation
    • Qualitative feedback collection
  • Production deployment on Hugging Face Spaces
  • 19 application screenshots documenting all features
  • 6 database structure screenshots showing MongoDB collections
  • Demo video on YouTube

Performance

  • SUS Score: 84.50 / 100 (Grade A - Excellent) 🏆
  • 92% real-world usage intent from user study
  • 88% of users rated system as "Good" or better
  • Sub-second response times maintained
  • 32 active users in database
  • 347 searches tracked
  • 139 favorites saved

Documentation

  • Comprehensive README (39KB, 999 lines)
  • User study results document
  • Full-stack application README
  • Quick start guide
  • Feature checklist
  • Setup scripts for Windows

[2.4.5] - 2026-01-12

Added - Multimodal RAG

  • Image query support - search with product images
  • Multimodal fusion retrieval using CLIP text + image embeddings
  • Visual-aware RAG responses (7.6 keywords per response)
  • V2.1 attribute integration (307K visual features)
  • 6 comprehensive notebooks covering full pipeline

Performance

  • Response time: 0.64s average (28% faster than v2.2)
  • 100% visual keyword generation rate
  • 6.0 unique products via multimodal fusion

[2.4.0] - 2026-01-05

Added - User Features & Personalization

  • User profile system with preferences and history
  • Content-based personalization (3 strategies)
  • Integrated agent system with intent awareness
  • Favorites management
  • Search history tracking

Performance

  • 76.7% preference matching accuracy
  • Sub-12ms personalization latency (target: <50ms)
  • 100% personalization coverage
  • 83.3% personalization rate in agent interactions

[2.3.0] - 2026-01-04

Added - AI Agents + LangChain

  • Conversational AI agent with ReAct reasoning
  • 3 specialized tools (SearchProducts, RecommendSimilar, GetProductDetails)
  • Conversation memory (10-turn sliding window)
  • Multi-turn dialogue support (5 scenarios tested)
  • Complete LangChain integration (4 notebooks, 82 cells)

Performance

  • 100% success rate on test dialogues
  • 100% tool usage rate
  • 2.6s average response time
  • Multi-turn context awareness

[2.2.0] - 2026-01-03

Added - RAG Pipeline

  • Production-ready RAG (Retrieval-Augmented Generation)
  • FashionRAGPipeline class with caching & batch processing
  • Framework-agnostic implementation
  • 3 professional notebooks (fundamentals, production, evaluation)

Performance

  • 0.714 average RAG score on 30 queries
  • 0.89s response time
  • Comprehensive evaluation metrics

[2.1.0] - 2026-01-01

Added - Core ML + Visual Attributes

  • Learned fusion optimization (α=0.7)
  • Visual attribute extraction (307K attributes, 10 categories)
  • Explainability system with fusion decomposition
  • Comprehensive query generation (104 bilingual queries)
  • 7 baseline methods comparison

Performance

  • 97.4% NDCG@10 (maintained from v2.0)
  • 95.4% product coverage with visual attributes
  • Attribute-aware search and reranking

[2.0.0] - 2025-12-15

Added - Stable Baseline

  • Core multimodal search system
  • Text embeddings (MPNet 768d)
  • Image embeddings (CLIP 512d → 768d aligned)
  • FAISS vector indexing
  • 30+ research notebooks
  • Production deployment pipeline
  • Docker containers
  • Monitoring with Prometheus + Grafana

Performance

  • 97.4% NDCG@10 baseline performance
  • 100% MRR on test queries
  • 51.1% Recall@10
  • Sub-10ms retrieval latency
  • 44,417 products indexed

Dataset

  • Fashion Product Images Dataset (Kaggle)
  • 44,417 products
  • 6 main categories
  • Multilingual support (Turkish/English)

[1.0.0] - 2025-09-01

Initial Development

  • Project kickoff
  • Dataset acquisition
  • Initial research and planning
  • Literature review
  • Technical feasibility study

Version Naming Convention

  • v2.x: GenAI-enhanced versions (January-February 2026)
  • v2.0: Stable baseline (September-December 2025)
  • v1.0: Initial development (September 2025)

Each minor version (2.1, 2.2, etc.) represents a complete feature addition documented in separate directories.

Project Timeline

v1.0  Sep 2025       Initial development
v2.0  Dec 2025       Stable baseline (97.4% NDCG@10)
v2.1  Jan 1, 2026    Visual attributes + explainability
v2.2  Jan 3, 2026    RAG pipeline
v2.3  Jan 4, 2026    AI agents + LangChain
v2.4  Jan 5, 2026    User features + personalization
v2.4.5 Jan 12, 2026  Multimodal RAG
v2.5  Jan 17, 2026   Full-stack + user study (SUS 84.50) 🏆

Links

Contributors

  • Hatice Baydemir - Student Researcher
  • İlya Kuş - Academic Advisor

License

This project is licensed under the MIT License - see the LICENSE file for details.

Last Updated: January 17, 2026