How to Build a Production-Ready Semantic Search Engine for E-Commerce in Python

A new developer tutorial outlines how to build a semantic search engine for e-commerce catalogues using open-source Python tools, including sentence-transformers, FAISS, and FastAPI. The guide explains how product data such as titles, descriptions, and attributes can be converted into dense vectors for efficient similarity-based search. For catalogues under one million SKUs, the tutorial recommends single-vector embedding using the all-MiniLM-L6-v2 model as the optimal balance of speed and accuracy. Beyond basic vector search, the tutorial also covers a hybrid re-ranking layer that incorporates business rules like inventory levels and profit margins alongside personalisation signals. The complete indexing pipeline is designed to run in under 100 lines of Python and return ranked results in under 100 milliseconds.