Edge-first AI Search App
AI / MLLow-latency AI search with edge functions, vector embeddings, and semantic caching
6 nodes6 connections
Use Case
AI-powered documentation search, knowledge bases, customer-facing Q&A, semantic search portals
Stack Breakdown
Next.js EdgeEdge FunctionsVector DBLLMSemantic Cache
Architecture Layers
1Edge Runtime
2Query Processing
3Semantic Caching
4Embedding & Retrieval
5Answer Generation
Components by Category
frontend
Next.js
infra
Edge Function
database
Semantic CacheVector DB
external
Embedding APILLM Provider
Why This Topology Works
Edge functions process queries close to users for low latency. Semantic cache avoids redundant LLM calls for similar queries. Vector DB provides context-aware retrieval for grounded answers.
Scaling Notes
Edge functions scale automatically with CDN provider. Semantic cache reduces LLM costs by 40-60%. Vector DB partitions by embedding namespace.
Observability
Track cache hit rate, embedding latency, vector search recall, LLM token usage, and end-to-end TTFB from edge.