Context
A regional edge deployment needs local Vector AI retrieval for latency-sensitive recommendations. Network connectivity to central systems is intermittent, and runtime resources are fixed.
Constraints
- Hard memory budget and limited storage throughput.
- Requirement for clear durability posture and auditable tradeoffs.
- Need for explicit API limits to prevent noisy-neighbor behavior.
- Operations team requires Prometheus-compatible metrics and runbooks.
Vector AI Design with AIONBD
- Use exact mode for quality-sensitive paths and IVF/auto for throughput paths.
- Use WAL sync-on-write for safety-first environments.
- Cap concurrency, request body size, and top-k to stabilize tail latency.
- Adopt batch endpoints for higher throughput Vector AI Search traffic.
Operations Model
Baseline SLOs are tied to readiness, 5xx ratio, checkpoint health, and IVF fallback ratio. The on-call workflow starts from metrics, then applies controlled mitigations before restarts.
Reference docs: operations_observability.md
Expected Results
- Predictable Vector AI response patterns under constrained load.
- Operationally explicit durability and recovery behavior.
- Improved throughput with batch search and tuned persistence settings.
- Lower debugging cost due to deterministic limits and stable metrics.