Cache DSPy in your AI search with Crowkis
Building AI search on DSPy? Add a semantic cache so popular queries hit again and again 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 AI search on DSPy? Add a semantic cache so popular queries hit again and again stop costing full price.
Building voice assistants on LangGraph? Add a semantic cache so latency-sensitive, repetitive spoken queries stop costing full price.
Add a semantic cache to DSPy so repeated and reworded questions are served for free, no rewrite, self-hosted.
A cache that only says 'hit' or 'miss' makes you trust it blindly. Crowkis returns a confidence score per hit, a geometric mean of five signals, so you decide the bar reuse must clear.
Live verdicts, hit-type economics, top misses, safety blocks, tenant accounting, system pressure, what each panel answers and who keeps it open.
Agents multiply model calls per user action by 10-50x. Without aggressive reuse, the unit economics of agentic products simply don't close.
Building contract analysis tools on LangChain.js? Add a semantic cache so the same clause questions across documents stop costing full price.
Building customer support bots on the OpenAI Python SDK? Add a semantic cache so repeat questions from every customer, all day stop costing full price.
Building ecommerce assistants on DSPy? Add a semantic cache so the same product and policy questions across shoppers stop costing full price.
Building email drafting tools on LangGraph? Add a semantic cache so similar drafts requested over and over stop costing full price.
Durable, per-user memory for DSPy agents that survives restarts and consolidates contradictions, self-hosted, zero egress.
Each regime wants specific retention, audit, and erasure behavior. Enterprise compliance modes preset the whole posture, so the auditor's checklist maps to a flag.
pgvector is a lovely extension for storing embeddings next to your data. Routing every LLM query through Postgres is how lovely things die.
Building onboarding assistants on LangChain.js? Add a semantic cache so every new hire asking the same first questions stop costing full price.
Building coding assistants on the OpenAI Python SDK? Add a semantic cache so the same explanations and boilerplate reasoning, dozens of times a day stop costing full price.
Building healthcare Q&A assistants on DSPy? Add a semantic cache so recurring policy and triage questions stop costing full price.
Building code review bots on LangGraph? Add a semantic cache so the same review patterns across pull requests stop costing full price.
Add a semantic cache to Instructor so repeated and reworded questions are served for free, no rewrite, self-hosted.
LRU evicts by recency and nothing else. But cache entries have wildly different replacement costs, and forgetting a $0.40 answer to keep a $0.0004 one is just bad accounting.
Money questions repeat endlessly and tolerate zero staleness. Fintech is where freshness control stops being a feature and becomes the product.