02 FAISS Vector Search in Python — Embeddings, Indexing, and Distance Matching Explained

Description

👉 Free Python Course: https://industry-python.thinkific.com/products/courses/industry-projects-with-python In this tutorial, we complete a full similarity search engine in Python using Sentence Transformers, NumPy, and FAISS — and run it on Apple Silicon using Metal Performance Shaders (MPS) for hardware acceleration. You’ll learn how to convert text into 384-dimensional embeddings, store them in a FAISS L2 index, and compare user queries using vector distance instead of keyword matching. This is the same foundation used in RAG pipelines, AI search systems, and production-grade retrieval engines. 🔧 What You’ll Learn - How MPS (Metal Performance Shaders) accelerates PyTorch on M1/M2 Macs - How to convert sentences into NumPy float32 embeddings - How to build a FAISS IndexFlatL2 (384-D) vector index - How to persist and reload FAISS indexes from disk - How to encode single vs multiple queries - How to interpret distance scores and ranking results - How similarity search becomes the retrieval layer for LLM + RAG systems 🧠 Pipeline Overview Text → Sentence Transformers → PyTorch (MPS) → NumPy (float32) → FAISS Index → Distance Match → Ranked Results 💻 Environment - macOS (Apple Silicon M1/M2/M3) - Python + uv - Sentence Transformers - PyTorch (MPS backend) - NumPy - FAISS (CPU index) 🎓 Free Course & Code This video is part of our 100% free AI engineering course where you’ll build: - Vector search engines - RAG pipelines - Local and cloud AI systems - GPU and CPU-compatible AI apps 👉 Enroll here: https://industry-python.thinkific.com/products/courses/industry-projects-with-python 🔔 Who This Is For - CS students learning vector math and embeddings - Developers building RAG systems and AI search engines - Mac users who want GPU-accelerated AI without NVIDIA hardware - Educators teaching AI infrastructure from first principles