2 ms·

1. why mixbread's model? 2. how much efficiency gain did you see binarising embeddings/using hamming distance? 3. why milvus over other vector stores? 4. did

by swyx 2y ago

1. why mixbread's model? 2. how much efficiency gain did you see binarising embeddings/using hamming distance? 3. why milvus over other vector stores? 4. did you automate the weekly metadata pull? just a simple cron job? anything else you need orchestrated? user thoughts on searching for "transformers on byte level not token level" - was good but didnt turn up https://arxiv.org/abs/2412.09871 https://arxiv.org/abs/2412.09871 <- which is more recent, more people might want also you might want more result density - so perhaps a UI option to collapse the abstracts and display more in the first glance.

Quizzical4230 2y ago
1. The model size was small enough to process the corpus fast-ish using the limited resources I have. They also support MRL and binary embeddings which help would be helpful in case I need to downsize on the VM size. 2. Close to 500ms. See [^1]. 3. This [^2] was the reason I went with milvus. I also assumed that more stars would result in a bigger community and hence faster bug discovery and fixes. And better feature support. 4. Yes, I automated the weekly pull here [^3]. Since I am constrained on resources available, I used HuggingFace Spaces to do the automation for me :) Although, the space keeps sleeping and to avoid that, I am planning keep calling the same space using api/gradio_client. Let's see how that goes. | which is more recent, more people might want Absolutely agree. I am planning to add a 'Recency' sorting option for the same. It should balance between similarity and the date published. | also you might want more result density - so perhaps a UI option to collapse the abstracts and display more in the first glance. Oh, I will surely look into it. Thank you so much for a detailed response. :D [1]: https://news.ycombinator.com/item?id=42507116#42509636 https://news.ycombinator.com/item?id=42507116#42509636 [2]: https://benchmark.vectorview.ai/vectordbs.html https://benchmark.vectorview.ai/vectordbs.html [3]: https://huggingface.co/spaces/bluuebunny/update_arxiv_embeddings https://huggingface.co/spaces/bluuebunny/update_arxiv_embedd...
- swyx 2y ago
  my pleasure, thank you for the reply! ive never used milvus or heard of mixbread so this was refreshing.

by swyx 2y ago

Quizzical4230 2y ago

1. The model size was small enough to process the corpus fast-ish using the limited resources I have. They also support MRL and binary embeddings which help would be helpful in case I need to downsize on the VM size. 2. Close to 500ms. See [^1]. 3. This [^2] was the reason I went with milvus. I also assumed that more stars would result in a bigger community and hence faster bug discovery and fixes. And better feature support. 4. Yes, I automated the weekly pull here [^3]. Since I am constrained on resources available, I used HuggingFace Spaces to do the automation for me :) Although, the space keeps sleeping and to avoid that, I am planning keep calling the same space using api/gradio_client. Let's see how that goes. | which is more recent, more people might want Absolutely agree. I am planning to add a 'Recency' sorting option for the same. It should balance between similarity and the date published. | also you might want more result density - so perhaps a UI option to collapse the abstracts and display more in the first glance. Oh, I will surely look into it. Thank you so much for a detailed response. :D [1]: https://news.ycombinator.com/item?id=42507116#42509636 https://news.ycombinator.com/item?id=42507116#42509636 [2]: https://benchmark.vectorview.ai/vectordbs.html https://benchmark.vectorview.ai/vectordbs.html [3]: https://huggingface.co/spaces/bluuebunny/update_arxiv_embeddings https://huggingface.co/spaces/bluuebunny/update_arxiv_embedd...

swyx 2y ago
my pleasure, thank you for the reply! ive never used milvus or heard of mixbread so this was refreshing.