When your system logs an error like “running engine: context deadline exceeded”, it means that a component (e.g. LLM request, retrieval, or tool call) took too long and timed out waiting for context or response. The first step is to identify which stage: was it the vector DB retrieval? model inference? external API fetch? Then you instrument timing for each stage: measure retrieval latency, model runtime, network delays.
Once you know which stage is slow, you can mitigate. If vector retrieval is slow, use smaller indexes, caching, approximate search, or prefetching. If model inference is slow, you might use shorter context, batching, or faster model variants. For tool calls, consider timeouts, fallback strategies, or asynchronous invocation. Also, you can insert deadlines upstream: abort queries early or degrade gracefully with partial context rather than waiting until final stage.
To prevent this error in design, build your pipelines with time budgets. Allocate expected maximum latencies to each stage, monitor cumulative delay, and fallback before total timeout. Use circuit breakers or timeout guards. In sum: diagnosing that error means measuring stage latencies, adjusting component performance, and adding safe fallback logic in your context pipeline.