Initial commit

packages/leann-backend-hnsw/third_party/faiss/tutorial/python/1-Flat.py

@@ -0,0 +1,29 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+
+d = 64                           # dimension
+nb = 100000                      # database size
+nq = 10000                       # nb of queries
+np.random.seed(1234)             # make reproducible
+xb = np.random.random((nb, d)).astype('float32')
+xb[:, 0] += np.arange(nb) / 1000.
+xq = np.random.random((nq, d)).astype('float32')
+xq[:, 0] += np.arange(nq) / 1000.
+
+import faiss                   # make faiss available
+index = faiss.IndexFlatL2(d)   # build the index
+print(index.is_trained)
+index.add(xb)                  # add vectors to the index
+print(index.ntotal)
+
+k = 4                          # we want to see 4 nearest neighbors
+D, I = index.search(xb[:5], k) # sanity check
+print(I)
+print(D)
+D, I = index.search(xq, k)     # actual search
+print(I[:5])                   # neighbors of the 5 first queries
+print(I[-5:])                  # neighbors of the 5 last queries