Cache LlamaIndex in your RAG document search with Crowkis
Building RAG document search on LlamaIndex? Add a semantic cache so the same questions re-running retrieval over the same corpus stop costing full price.
Notes from the nest · 980 posts
Engineering notes written by the people building Crowkis. Comparisons, use cases, economics, internals, security, operations, and nothing written just to rank.
Building RAG document search on LlamaIndex? Add a semantic cache so the same questions re-running retrieval over the same corpus stop costing full price.
Durable, per-user memory for the Anthropic SDK agents that survives restarts and consolidates contradictions, self-hosted, zero egress.
LSM compaction is where storage engines breed complexity. Crowkis ships exactly one strategy across three levels, chosen for cache workloads, closed for configuration.
At consumer scale, traffic converges on shared intents while costs and latency multiply by millions. The cache becomes load-bearing infrastructure.
Semantic caching has an obvious failure mode nobody likes to talk about: one bad write, served forever to everyone nearby. This is how Crowkis decides what to trust.
Building voice assistants on Spring AI? Add a semantic cache so latency-sensitive, repetitive spoken queries stop costing full price.
Building sales enablement tools on the OpenAI Python SDK? Add a semantic cache so reps asking the same product questions stop costing full price.
Building SQL generation tools on DSPy? Add a semantic cache so the same schema questions and query shapes stop costing full price.
Building internal copilots on LlamaIndex? Add a semantic cache so employees asking overlapping questions of the same knowledge base stop costing full price.
Add a semantic cache to the Gemini SDK so repeated and reworded questions are served for free, no rewrite, self-hosted.
The reasoning is the expensive part of a hard answer. CTHINK stores a chain-of-thought trace as a reusable step graph; CREUSE fetches the matching plan for a new query at a fraction of the original token cost.
Building email drafting tools on Spring AI? Add a semantic cache so similar drafts requested over and over stop costing full price.
Building knowledge base assistants on the OpenAI Python SDK? Add a semantic cache so the same lookups across a team all day stop costing full price.
Building devops copilots on DSPy? Add a semantic cache so the same runbook and incident questions stop costing full price.
Building chatbots on LlamaIndex? Add a semantic cache so high-volume conversational traffic that repeats constantly stop costing full price.
Durable, per-user memory for the Gemini SDK agents that survives restarts and consolidates contradictions, self-hosted, zero egress.
Anthropic's prompt caching is excellent at its actual job, cheap long contexts. It was never designed to be your response cache, and the pricing says so.
Building code review bots on Spring AI? Add a semantic cache so the same review patterns across pull requests stop costing full price.
Building research assistants on the OpenAI Python SDK? Add a semantic cache so overlapping literature and summary questions stop costing full price.
Building API documentation bots on DSPy? Add a semantic cache so the same endpoint questions from every developer stop costing full price.