How I Architected a Multi-Provider Fallback for Local RAG (opens in new tab)

Working with local LLMs via Ollama is great for privacy, but it introduces a reliability bottleneck: local compute resources aren't always available or fast enough for complex inference. Recently, I built a local-first RAG (Retrieval-Augmented Generation) tool called Study Assistant to manage my personal document library. During development, I realized that relying solely on a single local model wasn't robust enough for my needs. I wanted a system that could "gracefully degrade"—if local comp...