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As we are working to integrate RAFT into FAISS, we need to remove all dependence on FAISS to avoid a circular dependency problem. Since the code is pretty close to optimal for moderate values of k, we might be better off just copying the k-selection code into RAFT for the time being (providing proper attribution to FAISS, another with the appropriate licenses, of course).
Essentially, we need to port the bfknn to use RAFT primitives. Successful implementation of this issue will include all of the distances which are currently supported by bfknn and replacement of bfknn from FAISS in our brute_force_knn with our own version. Ideally, we would also provide more customization around the batch sizes in order to make our API a little more flexible.
The text was updated successfully, but these errors were encountered:
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As we are working to integrate RAFT into FAISS, we need to remove all dependence on FAISS to avoid a circular dependency problem. Since the code is pretty close to optimal for moderate values of k, we might be better off just copying the k-selection code into RAFT for the time being (providing proper attribution to FAISS, another with the appropriate licenses, of course).
Essentially, we need to port the
bfknn
to use RAFT primitives. Successful implementation of this issue will include all of the distances which are currently supported bybfknn
and replacement ofbfknn
from FAISS in ourbrute_force_knn
with our own version. Ideally, we would also provide more customization around the batch sizes in order to make our API a little more flexible.The text was updated successfully, but these errors were encountered: