A point of note is that the text embeddings model used here is paraphrase-multilingual-MiniLM-L12-v2 ( https://huggingface.co/sentence-transformers/paraphrase-mult... ), which is about 4 years old. In the NLP world, that's effectively ancient, particularly as the robustness of even small embeddings models due to global LLM improvements has increased dramatically both in information representation and distinctiveness…
Totally fair — I defaulted to paraphrase-multilingual-MiniLM-L12-v2 mostly for speed and wide compatibility, but you’re right that it’s long in the tooth by today’s standards. I’d be really curious to see how something like all-mpnet-base-v2 or even text-embedding-ada-002 would behave, especially if we keep the suffixes in and lean into full contextual embeddings rather than reducing to root forms. Appreciate you cal…
I recommend ollama to run the artic-embed-v2 model, it also is multimingual and you can use --quantize when loading the modelfile to get it even smaller.