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?
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?
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.
So these are nearest neighbor search implementations, not database backends.