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

Short summary

A practical tutorial for building a production-ready semantic search engine for e-commerce using sentence-transformers, FAISS, and FastAPI. Covers embedding pipelines for product catalogues, the tradeoff between single-vector and late-interaction models like ColBERT, and a re-ranking layer that blends semantic relevance with business rules such as margin, inventory, and personalization. Targets sub-100ms query latency for catalogues under 1 million SKUs.

• •Build semantic search with sentence-transformers, FAISS, and FastAPI in under 100 lines of Python
• •Covers embedding pipeline, single-vector vs ColBERT tradeoffs, and hybrid re-ranking with business rules
• •Targets sub-100ms latency for catalogues under 1M SKUs

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