Semantic Caching

Generative UI architecture brings a new horizon for user experience, but it is the worst nightmare for QA and DevOps teams. How do you write an automated test script (E2E Test) for an interface when you don’t know what content the AI will generate beforehand? And how do you ensure the system doesn’t burn through API budgets when thousands of users ask the exact same question? 6.1. The Non-deterministic Hurdle in E2E Testing In traditional (Deterministic) applications, a Cypress or Playwright test script usually looks like this: ...

In Part 5: Critique Loop - Preventing LLM Hallucination, we successfully built an automated response auditing module to ensure logical accuracy. However, when deploying this Agentic Search system to a large-scale production environment serving millions of users, you will immediately face practical operational challenges: Unit Economics: Every user search going through multiple LLM calls (from generating answers, calling tools, to self-critiquing) will skyrocket API bills. Latency: Customers won’t patiently wait 5-10 seconds to receive the complete final answer. Observability: How do you trace which nodes a request went through, how many tokens it consumed, and where it encountered errors? The final article in this series will guide you on thoroughly solving these problems by integrating Semantic Caching (Redis), Deterministic Model Routing, Server-Sent Events (SSE) Streaming, and OpenTelemetry Tracing into the Eino (CloudWeGo) framework. ...

1. Introduction: The Failure of Mechanical Chunking When building a RAG system, if you only split documents using traditional functions like RecursiveCharacterTextSplitter (e.g., slicing every 500 tokens), you are destroying your system. Mechanical slicing disrupts pronouns (“it”, “they”, “this project”) and completely causes context loss. A paragraph explaining “Compensation” on page 10 will be completely meaningless to an LLM if it is severed from the “Contract Name and Stakeholders” located on page 1. ...