How to Build Scalable Semantic Search with Embeddings and Vector DBs
The guide walks through choosing embedding models, similarity metrics, ANN algorithms, and vector database options to deploy production‑grade semantic search for knowledge bases, product catalogs, or RAG pipelines. It shows concrete code snippets and performance trade‑offs, helping engineers cut latency and cost while delivering meaning‑based results at million‑document scale.