chore: import upstream snapshot with attribution

test/python/testvectors/testdense/testcustom.py

@@ -0,0 +1,51 @@
+"""
+Custom module tests
+"""
+
+import os
+import unittest
+
+import numpy as np
+
+from txtai.vectors import VectorsFactory
+
+
+class TestCustom(unittest.TestCase):
+    """
+    Custom vectors tests
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        """
+        Create custom vectors instance.
+        """
+
+        cls.model = VectorsFactory.create({"method": "txtai.vectors.HFVectors", "path": "sentence-transformers/nli-mpnet-base-v2"}, None)
+
+    def testIndex(self):
+        """
+        Test transformers indexing
+        """
+
+        # Generate enough volume to test batching
+        documents = [(x, "This is a test", None) for x in range(1000)]
+
+        ids, dimension, batches, stream = self.model.index(documents)
+
+        self.assertEqual(len(ids), 1000)
+        self.assertEqual(dimension, 768)
+        self.assertEqual(batches, 2)
+        self.assertIsNotNone(os.path.exists(stream))
+
+        # Test shape of serialized embeddings
+        with open(stream, "rb") as queue:
+            self.assertEqual(np.load(queue).shape, (500, 768))
+
+    def testNotFound(self):
+        """
+        Test unresolvable vector backend
+        """
+
+        with self.assertRaises(ImportError):
+            VectorsFactory.create({"method": "notfound.vectors"})