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_swigfaissZ_swigfaiss_avx2Z_swigfaiss_avx512Z_swigfaiss_avx512_sprZlibfaiss_python_callbacksZ_swigfaiss_sveZ_faiss_example_external_modulezCould not find z or z . Faiss may not be compiled yet.zCopying zswigfaiss.pyzfaiss/swigfaiss.pyzfaiss/_swigfaisszswigfaiss_avx2.pyzfaiss/swigfaiss_avx2.pyzfaiss/_swigfaiss_avx2zswigfaiss_avx512.pyzfaiss/swigfaiss_avx512.pyzfaiss/_swigfaiss_avx512zswigfaiss_avx512_spr.pyzfaiss/swigfaiss_avx512_spr.pyzfaiss/_swigfaiss_avx512_sprzfaiss/zswigfaiss_sve.pyzfaiss/swigfaiss_sve.pyzfaiss/_swigfaiss_svez faiss_example_external_module.pyz&faiss/faiss_example_external_module.pyz$faiss/_faiss_example_external_modulea  
Faiss is a library for efficient similarity search and clustering of dense
vectors. It contains algorithms that search in sets of vectors of any size,
 up to ones that possibly do not fit in RAM. It also contains supporting
code for evaluation and parameter tuning. Faiss is written in C++ with
complete wrappers for Python/numpy. Some of the most useful algorithms
are implemented on the GPU. It is developed by Facebook AI Research.
z1.10.0zIA library for efficient similarity search and clustering of dense vectorsz)https://github.com/facebookresearch/faissz9Matthijs Douze, Jeff Johnson, Herve Jegou, Lucas Hosseinizfaiss@meta.comMITzsearch nearest neighborsnumpy	packaging)r   zfaiss.contribzfaiss.contrib.torchz*.soz*.pydF)nameversiondescriptionlong_descriptionurlauthorauthor_emaillicensekeywordsZinstall_requirespackagespackage_dataZzip_safe)!
__future__r   osplatformshutil
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