A developer claims to have found a method to migrate between embedding models with zero downtime, addressing the challenge of upgrading models when dealing with large document collections. The approach is presented as a practical solution for retrieval-augmented generation (RAG) systems, particularly in local LLM contexts, to maintain grounded answers while avoiding service interruptions.
So I've been messinga round with embedding models for a bit, and I think they are interesting enough to experiment with. They are useful for rag, especially in a localllm sense because you can ground your answers in truth. But what happens if you have a billion documents, and you decide to upgrade y
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