Add Python HNSW tutorial (#5260)

Summary:
This PR adds a Python HNSW tutorial corresponding to the existing C++ HNSW tutorial.

The example creates synthetic vectors, builds an IndexHNSWFlat index, runs a search, and prints the nearest-neighbor IDs and distances for the final queries.

Pull Request resolved: https://github.com/facebookresearch/faiss/pull/5260

Reviewed By: mnorris11

Differential Revision: D107272616

Pulled By: alibeklfc

fbshipit-source-id: 721d7a1a95aa79150d6cb9653bd803065c82dcd7

tutorial/python/6-HNSW.py

@@ -0,0 +1,41 @@
+# 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
+
+import faiss
+
+
+d = 64       # dimension
+nb = 100000  # database size
+nq = 10000   # number of queries
+
+np.random.seed(1234)
+
+xb = np.random.random((nb, d)).astype("float32")
+xb[:, 0] += np.arange(nb) / 1000.0
+
+xq = np.random.random((nq, d)).astype("float32")
+xq[:, 0] += np.arange(nq) / 1000.0
+
+k = 4
+
+index = faiss.IndexHNSWFlat(d, 32)  # M=32: neighbors per node
+index.hnsw.efConstruction = 40      # graph construction quality
+index.add(xb)
+index.hnsw.efSearch = 64            # higher = better recall, slower
+
+# sanity check
+D, I = index.search(xb[:5], k)
+print(I)
+print(D)
+
+D, I = index.search(xq, k)
+
+print("I=")
+print(I[-5:])
+
+print("D=")
+print(D[-5:])