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This looks like a great package. Many vector-search engines do not allow you to implement your own custom distance metrics. But Unum does. Love it!

ashvardanian 3y ago
Oh, thank you! The library author here :) We've just hosted one of our first community/contributor calls a few hours ago, discussing the plans for the upcoming 1.0 release, and integration with UCall, UStore, and UForm - our other FOSS libraries. Please don't hesitate to reach out for any questions or feature requests - now is the best time :)
- _0ffh 3y ago
  I know it's a triviality, but you've got a typo in "Hardware-agmostic" you may want to fix.
  - ashvardanian 3y ago
    Thanks for suggestion! Details count, nothing is a triviality! If someone here has free time and C++ experience I am open to recommendations on the codebase and style as well: https://github.com/unum-cloud/usearch/blob/main-dev/include/usearch/index.hpp https://github.com/unum-cloud/usearch/blob/main-dev/include/...
    - pilooch 3y ago
      Look at policy-based templating, my goto for generic AI/search algorithms with many options. May prove useful.... Or not :)
- gregw134 3y ago
  I Have a general vector retrieval question, if you have time to humor me. Suppose I have 10 features per document, each with an embedding. Is it possible to retrieve the document with the highest average embedding score across its features? The only approach I can think of is retrieving the top 1k results across each feature to generate a candidate set, then recomputing full scores for each document.
  - ashvardanian 3y ago
    e z! The simplest way with USearch - concatenate 10 embeddings, define a custom metric with Numba, that takes the average of 10 dot-products. Done :)
    - gregw134 3y ago
      Cool. What if I want a weighted average of embeddings? Going further, is it possible to adjust the weights at search time?
      - ashvardanian 3y ago
        Yes, and yes. The last one may be a bit trickier through Python bindings today, but I can easily include that in the next release… shouldn’t take more than 50 LOC.
    - gregw134 3y ago
      (replying here because hn limits thread length) > Yes, and yes. The last one may be a bit trickier through Python bindings today, but I can easily include that in the next release… shouldn’t take more than 50 LOC. Appreciate it. That'd be game-changing for me. The ultimate thing I'd like to do is actually use a function of the form score = af1(embedding1) + bf2(embedding2) + ... That way you could make adjustments like ignoring feature1 unless its score passes a threshold. I'll try looking at Numba to see if that's possible.
      - ashvardanian 3y ago
        Sure, don’t hesitate to reach out to us on Discord. It will be much easier to chat and exchange code snippets there: https://discord.gg/A6wxt6dS9j https://discord.gg/A6wxt6dS9j

ashvardanian 3y ago

Oh, thank you! The library author here :) We've just hosted one of our first community/contributor calls a few hours ago, discussing the plans for the upcoming 1.0 release, and integration with UCall, UStore, and UForm - our other FOSS libraries. Please don't hesitate to reach out for any questions or feature requests - now is the best time :)

_0ffh 3y ago
I know it's a triviality, but you've got a typo in "Hardware-agmostic" you may want to fix.
- ashvardanian 3y ago
  Thanks for suggestion! Details count, nothing is a triviality! If someone here has free time and C++ experience I am open to recommendations on the codebase and style as well: https://github.com/unum-cloud/usearch/blob/main-dev/include/usearch/index.hpp https://github.com/unum-cloud/usearch/blob/main-dev/include/...
  - pilooch 3y ago
    Look at policy-based templating, my goto for generic AI/search algorithms with many options. May prove useful.... Or not :)
gregw134 3y ago
I Have a general vector retrieval question, if you have time to humor me. Suppose I have 10 features per document, each with an embedding. Is it possible to retrieve the document with the highest average embedding score across its features? The only approach I can think of is retrieving the top 1k results across each feature to generate a candidate set, then recomputing full scores for each document.
- ashvardanian 3y ago
  e z! The simplest way with USearch - concatenate 10 embeddings, define a custom metric with Numba, that takes the average of 10 dot-products. Done :)
  - gregw134 3y ago
    Cool. What if I want a weighted average of embeddings? Going further, is it possible to adjust the weights at search time?
    - ashvardanian 3y ago
      Yes, and yes. The last one may be a bit trickier through Python bindings today, but I can easily include that in the next release… shouldn’t take more than 50 LOC.
  - gregw134 3y ago
    (replying here because hn limits thread length) > Yes, and yes. The last one may be a bit trickier through Python bindings today, but I can easily include that in the next release… shouldn’t take more than 50 LOC. Appreciate it. That'd be game-changing for me. The ultimate thing I'd like to do is actually use a function of the form score = af1(embedding1) + bf2(embedding2) + ... That way you could make adjustments like ignoring feature1 unless its score passes a threshold. I'll try looking at Numba to see if that's possible.
    - ashvardanian 3y ago
      Sure, don’t hesitate to reach out to us on Discord. It will be much easier to chat and exchange code snippets there: https://discord.gg/A6wxt6dS9j https://discord.gg/A6wxt6dS9j