Edge-first AI Search App
AI / MLProductionGlobal low-latency searchEdge-deployed AI search with serverless query processing, embedding-keyed semantic cache, vector retrieval grounding, and OpenTelemetry request tracing. Suitable for low-latency AI search products.
Recommended for: AI search experiences with strict latency budgets
Scope Notes
Intended for AI search experiences with strict latency budgets at Production complexity and Global low-latency search scope. Treat the graph as a starting shape rather than a fixed stack: keep the topology and the signals below, then swap providers to match your platform.
Review Checklist
Use Case
AI-powered documentation search, knowledge bases, customer-facing Q&A, semantic search portals
Best Fit Scenarios
- Public docs and help-centre search
- Semantic lookup with edge response paths
- Teams optimising both relevance and token cost
Stack Breakdown
Architecture Layers
Components by Category
frontend
infra
database
external
Why This Topology Works
Edge functions terminate queries close to the user and check the embedding-keyed Semantic Cache before spending anything on embeddings or generation. Cache misses embed the query, retrieve context from the Vector DB, and generate an answer — but the embedding and generation legs are backhaul calls to regional providers, so the edge tier removes round trips without pretending inference is local.
Scaling Notes
Edge functions scale with the provider's network. Semantic Cache is only effective if results and embeddings are written back with a TTL, and Vector DB partitions by embedding namespace.
Observability
Track cache hit rate, embedding latency, vector recall and LLM token usage, with end-to-end TTFB from the edge. OpenTelemetry spans cover the backhaul legs, which are the slow segment.
Typical Bottlenecks
- Embedding and LLM backhaul latency from the edge
- Semantic cache miss rate for novel queries
- Cost of uncontrolled embedding and generation calls
Async Flow and Reliability
The edge function serves the happy path from cache. On a miss, embedding, retrieval and generation run synchronously because the answer is the response, while cache writes and traces happen alongside.
Upgrade Path
Add edge authentication and per-client rate limiting, a fallback LLM provider for provider outages, and an ingestion/indexer path that keeps the Vector DB current.