Your RAG knowledge base is silently rotting. Embedding models change, source documents go stale, chunks corrupt — and retrieval quality degrades with no alert, no detection, no self-healing. Meanwhile, Kubernetes can autoscale your pods on CPU, but has no idea that one "heavy" analytical query costs 50x more than a simple lookup.

This talk introduces a Kubernetes operator and CRD that manages RAG platforms the way we manage databases — declaratively. Change your embedding model or chunk size in YAML, and the operator performs a zero-downtime shadow-collection swap. It also classifies queries by semantic complexity and scales each pipeline component independently: a heavy query scales up LLM inference without touching the embedding service.

Attendees will see how a single CRD unifies knowledge base health and query-aware scaling into one closed-loop controller, no multi-tool glue, no propagation delay.