Developer's embedding server failed silently for two weeks thanks to smart fallbacks

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A developer running a personal AI documentation search tool called Codicil discovered that its embedding server had been offline for roughly two weeks without any apparent disruption. The tool, which normally uses semantic vector search via an Ollama-powered embedding model, had quietly fallen back to keyword-based file grepping after the embedding endpoint went dark. Because the fallback returned plausible results, the developer — also the sole user and monitor — had no reason to suspect anything was wrong. Investigating the silent failure led to a review of the codebase's resilience features, including graceful degradation, crash-safe reindexing, and a file-lock fix to prevent race conditions between concurrent processes. The developer also proactively patched a latent bug where switching embedding models against the same data collection could cause silent write failures due to mismatched vector dimensions.

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