Ruslan Magana Vsevolodovna • 6/2/2026

Build a Semantic Search Engine in Python with Sentence Transformers, FAISS, and Embeddings

This tutorial guides readers through building a semantic search engine in Python, leveraging Sentence Transformers to convert text into embeddings and FAISS for efficient nearest-neighbor search. It covers creating a support-FAQ corpus with metadata (ID, title, category), generating embeddings, indexing with FAISS, and performing queries that match meaning rather than exact keywords. The article includes setup instructions, code examples, and explains how to persist the index for reuse, with a bridge to RAG pipelines. Suitable for developers interested in NLP, search systems, and Python-based AI tools.

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#Python #Embeddings #Semantic Search