MLX Embedders: From Theory to Production-Ready Understanding
In Part 1 of this series, we built a minimal LLM inference pipeline on Apple Silicon using MLX Swift. In Part 2, we quantized a model from scratch and saw how 4-bit precision makes billion-parameter models tractable on a phone. This article takes a different angle. Instead of generating text, we are going to encode meaning. A user types: “best hikes near a volcano.” A keyword search returns nothing useful. An embedding-based system returns exactly what they need, because it understands what the words mean — not just what they spell. That capability is what embeddings unlock, and it is the foundation of every serious AI feature in production today: semantic search, recommendation systems, RAG pipelines, and clustering. ...