guidesMay 8, 2026· 4 min read

Cache Spring AI in your RAG document search with Crowkis

Building RAG document search on Spring AI? Add a semantic cache so the same questions re-running retrieval over the same corpus stop costing full price.

RAG document search built on Spring AI share one problem: the same questions re-running retrieval over the same corpus. Each repeat is a full-price Spring AI call for an answer you already have.

Put a semantic cache in front. Point Spring AI's base URL at the Crowkis OpenAI-compatible gateway, or wrap the call in get-or-compute, and reworded repeats are served from cache, no rewrite of your RAG document search logic.

Spring AI + Crowkis

base_url = "http://127.0.0.1:6380/v1"   # Crowkis gateway, semantic cache in front

In plain words: The RAG document search keep working exactly as before; the repeats just stop hitting the model.

On repetitive traffic this cuts costs up to 60-70% on repetitive workloads, and every hit carries a confidence score so reuse stays safe. Runs self-hosted with zero egress, nothing leaves your machine.