i#\SSKrSSK7 S SjrS SjrS SjrSS\SSSS4SjrS\S4S jrg) N)*cUc[[U55n[5n[5n[ X05H'upgUR U5 UR U5 M) [ U[5(a [XEX5$[XEX5$)zbuilds the C++ vectors for the GPU indices and the resources. Handles the case where the resources are assigned to the list of GPUs ) rangelenGpuResourcesVector Int32Vectorzip push_back isinstance IndexBinary index_binary_cpu_to_gpu_multipleindex_cpu_to_gpu_multiple) resourcesindexcogpusvresvdeviress b/home/dmtnaga/Documents/work/airagagent/rag_env/lib/python3.13/site-packages/faiss/gpu_wrappers.pyindex_cpu_to_gpu_multiple_pyrsx |S^$  D =Dd& q s'%%%/EFF(U??c[XSUS9nU$)N)rrngpu)index_cpu_to_gpus_list)rrr index_gpus rindex_cpu_to_all_gpusr&s&u$TJI rcUcUS:Xa[[55nOUcUS:wa [U5nUVs/sH n[5PM nn[XPX5nU$s snf)zHere we can pass list of GPU ids as a parameter or ngpu to use first n GPU's. gpus mut be a list or None. co is a GpuMultipleClonerOptions r)r get_num_gpusStandardGpuResourcesr)rrrr_rrs rrr+s^ 42:\^$ ,TRZT{+/ 04a  !4C 0,SBI  1sAFc URupURR(aSn OAURR(aURnSn O[ R "USS9nSn [U5nUR[ R:Xa[nO0UR[ R:Xa[nO [S5eURunnUU :XdeURR(aSnOAURR(aURnSnO[ R "USS9nSn[U5nUR[ R:Xa[nO0UR[ R:Xa[nO [S5eUc%[ R"X4[ RS9nO3URX4:XdeUR[ R:Xde[U5nUc%[ R"X4[ RS9nOURX4:Xde[U5nUR[ R:Xa[ nODURURs=:Xa[ R":Xa O O[$nO [S5e['5nUUlUUlU UlUUlUUlUUlUUlUUlU UlUUlU UlUUlUUl UUl!UUl"UUl#U S:dU S:a[IUUX5 XE4$[KUU5 XE4$) ac Compute the k nearest neighbors of a vector on one GPU without constructing an index Parameters ---------- res : StandardGpuResources GPU resources to use during computation xq : array_like Query vectors, shape (nq, d) where d is appropriate for the index. `dtype` must be float32. xb : array_like Database vectors, shape (nb, d) where d is appropriate for the index. `dtype` must be float32. k : int Number of nearest neighbors. D : array_like, optional Output array for distances of the nearest neighbors, shape (nq, k) I : array_like, optional Output array for the nearest neighbors, shape (nq, k) metric : MetricType, optional Distance measure to use (either METRIC_L2 or METRIC_INNER_PRODUCT) device: int, optional Which CUDA device in the system to run the search on. -1 indicates that the current thread-local device state (via cudaGetDevice) should be used (can also be set via torch.cuda.set_device in PyTorch) Otherwise, an integer 0 <= device < numDevices indicates the GPU on which the computation should be run vectorsMemoryLimit: int, optional queriesMemoryLimit: int, optional Memory limits for vectors and queries. If not 0, the GPU will use at most this amount of memory for vectors and queries respectively. Vectors are broken up into chunks of size vectorsMemoryLimit, and queries are broken up into chunks of size queriesMemoryLimit, including the memory required for the results. Returns ------- D : array_like Distances of the nearest neighbors, shape (nq, k) I : array_like Labels of the nearest neighbors, shape (nq, k) TFfloat32dtypezxq must be f32 or f16xb must be float32 or float16zI must be i64 or i32r)&shapeflags c_contiguous f_contiguousTnpascontiguousarrayswig_ptrr'r%DistanceDataType_F32float16DistanceDataType_F16 TypeErroremptyint64IndicesDataType_I64int32IndicesDataType_I32GpuDistanceParamsmetrickdimsvectorsvectorsRowMajor vectorType numVectorsqueriesqueriesRowMajor queryType numQueries outDistances outIndicesoutIndicesTypedeviceuse_cuvs bfKnn_tilingbfKnn)rxqxbr<DIr;rIrJvectorsMemoryLimitqueriesMemoryLimitnqd xq_row_majorxq_ptrxq_typenbd2 xb_row_majorxb_ptrxb_typeD_ptrI_ptrI_typeargss rknn_gpura;sX HHEB xx    TT  ! !"I 6 b\F xx2::& RZZ &/00 XXFB 7N7 xx    TT  ! !"I 6 b\F xx2::& RZZ &788y HHbWBJJ /ww2'!!!ww"**$$$ QKEy HHbWBHH -ww2'!!! QKEww"(($ AGG 'rxx '$.//  DDK DFDIDL'DDODODL'DDNDODDO DDKDMA!3a!7S$ 2G 4K c4 4KrcURupgURR(aSnO5URR(aURnSnO [ S5e[ U5n UR[R:Xa[n Or?r@rArBrCrDrErFrIrL)rrMrNrOr;rIrSrTrUrVrWrXrYrZr[r\r]r`s rpairwise_distance_gpurcs< HHEB xx    TT  CE Eb\F xx2::& RZZ &  ! !"I 6 XXFB 7N7 xx    TT  ! !"I 6 b\F xx2::& RZZ &788y HHbXRZZ 0ww2("""ww"**$$$ QKE  DDK DFDIDL'DDODODL'DDNDODDK #t Hr)NN)Nr)NNr) numpyr. faiss.loaderrrr METRIC_L2rarcrrrhsK@"  #d9RRWlmCDCP*.ib r