The Kubernetes community's announcement of Ingress NGINX's retirement in March 2026 has created an urgent need for migration planning across thousands of production clusters. With no security patches, bug fixes, or updates coming after the final v1.15.1 release, organizations must act now to avoid running unmaintained software with escalating security risks. This isn't just about swapping one ingr
Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? The core idea of semantic chunking is to split text at semantic boundaries. Determining whether "two pieces of text belong to the same topic" requires converting text into vectors and computing similarity — that's exactly what the Embedding API does. Dimension Fixed-Length / Recur
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E