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Some questions 1. When you say backends, do you plan to integrate like a client with some "vector" stores. 2. Also any benchmarks? 3. Lastly, why python?
by aravindputrevu 2y ago
Some questions
1. When you say backends, do you plan to integrate like a client with some "vector" stores.
2. Also any benchmarks?
3. Lastly, why python?
- Pringled 2y ago1: that could be something for the future, but at the moment this is just meant as a way to quickly try out and evaluate various algorithms and libraries without having to learn the syntax for them (we call those backends). 2: we adopted the same methodology as ann-benchmarks for our evaluation, so technically the benchmarks there are valid for the backends we support. However it's a good suggestion to add those explicitly to the repo, I'll add a todo for that. 3: mainly because a: it's the language we are most the comfortable with developing in, b: it's the most widely used and adopted language for ML and c: (almost) all the algorithms we support are written in C/C++/Cython already.
- newfocogi 2y agoNot author of the library, but the documentation lists the backends here: https://github.com/MinishLab/vicinity?tab=readme-ov-file#supported-backends https://github.com/MinishLab/vicinity?tab=readme-ov-file#sup... So these are nearest neighbor search implementations, not database backends.