Both - tagging errors into broad buckets can help with filtering and searching for categories of errors, while finding similar errors surfaces ones that might b

Both - tagging errors into broad buckets can help with filtering and searching for categories of errors, while finding similar errors surfaces ones that might be more familiar to an unknown one. We're learning as we go though and also want to surface solutions to errors long term. As for hosting the model, Hugging Face has a number of guides (ie. [1]) for this but overall the process was quite simple. All you need to do is find a model compatible with the kind of inference you want to do (ie. sentence embeddings vs. text generation). Once it's deployed, Hugging Face gives you an API endpoint similar to OpenAI's embeddings API that returns the embedding vector for a given input. For picking the model, there are a bunch of good resources with benchmarks comparing them depending on the use case (ie. [2]). [1] https://huggingface.co/blog/getting-started-with-embeddings https://huggingface.co/blog/getting-started-with-embeddings [2] https://supabase.com/blog/fewer-dimensions-are-better-pgvector https://supabase.com/blog/fewer-dimensions-are-better-pgvect...