I used MixedBread's [^1] embedding model to generate vectors from the abstracts. I store and search similar vectors using Milvus [^2] and finally use Gradio [^3] to serve the frontend. I update the vector database weekly by pulling the metadata dataset from Kaggle [^4].
To speed up the search process on my free oracle instance, I binarise the embeddings and use Hamming distance as a metric.
I would love your feedback on the site :) Happy Holidays!
[1]: https://www.mixedbread.ai/docs/embeddings/mxbai-embed-large-... [2]: https://milvus.io/ [3]: https://www.gradio.app/ [4]: https://www.kaggle.com/datasets/Cornell-University/arxiv