Building a Semantic Search Engine with Hugging Face Transformers and MongoDB Atlas Vector Search

Semantic search enables meaning-based retrieval across languages by converting text into vector embeddings using transformer models. This tutorial demonstrates building a multilingual semantic search engine using Hugging Face's paraphrase-multilingual-MiniLM-L12-v2 model and MongoDB Atlas Vector Search. The implementation covers downloading a fashion product dataset, generating 384-dimensional embeddings, storing them in MongoDB, and exposing search functionality via FastAPI. Test queries in French and Polish successfully retrieve relevant English product descriptions, demonstrating cross-language semantic understanding without explicit translation.