README.md

metadata

title: MindMatch
emoji: 🚀
colorFrom: red
colorTo: red
sdk: docker
app_port: 8501
tags:
  - streamlit
pinned: false
short_description: 'Hybrid dating recommendation system combining collaborative '
license: mit

Welcome to Streamlit!

Edit /src/streamlit_app.py to customize this app to your heart's desire. :heart:

If you have any questions, checkout our documentation and community forums.

MatchMind: Dating Profile Recommendation System

Overview

MatchMind is a hybrid dating recommendation system that combines collaborative filtering and content-based approaches to deliver high-quality match suggestions. The system generates text-based user profiles, encodes them into vector embeddings, and stores them in a Qdrant vector database. It retrieves potential matches using semantic search and applies a reranking step with a cross-encoder model for improved match quality. The system mirrors production-grade matching logic and supports benchmarking with LLMs.

What the Project Does

• Profile Generation: Converts structured user data and Q&A into natural language profile templates.
• Embedding: Uses the nomic-ai/nomic-embed-text-v1.5 model (SentenceTransformer) to generate 768-dimensional embeddings for each profile.
• Vector Database: Stores and searches embeddings in Qdrant (collections: dating_M, dating_F).
• Semantic Search: Retrieves top candidate matches using vector similarity (cosine distance).
• Reranking: Uses the cross-encoder model cross-encoder/ms-marco-MiniLM-L-6-v2 to rerank the top candidates for final recommendations.
• Multi-Signal Scoring: Supports additional signals and benchmarking for match quality.
• Web App: Streamlit interface for user profile entry, Q&A, match discovery, and full profile viewing.
• Benchmarking: Optional LLM-based scoring for research and evaluation.

Models and Technologies Used

• Embedding Model: nomic-ai/nomic-embed-text-v1.5 (SentenceTransformer)
• Reranking Model: cross-encoder/ms-marco-MiniLM-L-6-v2 (CrossEncoder)
• Vector Database: Qdrant (collections: dating_M, dating_F)
• Backend: Python, Flask, SQLAlchemy, MySQL
• Frontend: Streamlit
• Benchmarking (optional): LLMs via Ollama

Installation

1. Clone the repository.
2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up environment variables in a .env file (see config.py for required keys).

Try the Web Demo

You can try out the MatchMind web app here: https://ultron3002-mindmatch.hf.space/

Project Structure

• app.py — Streamlit web interface
• main.py — Core ranking and embedding logic
• config.py — Configuration and environment variables
• db/ — Database connection and SQL utilities
• utils/ — Embedding, ranking, and Qdrant utilities
• page_modules/ — Streamlit page components
• benchmarking/ — LLM benchmarking scripts
• migration_notebooks/, templating_notebooks/ — Data and template notebooks

Requirements

See requirements.txt for all dependencies.

License

MIT License