guidesJuly 5, 2026· 4 min read

Cache LiteLLM in your RAG document search with Crowkis

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

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

Put a semantic cache in front. Point LiteLLM'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.

LiteLLM + 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. Drop it in over RESP, gRPC, REST, or MCP, no rewrite required.