def testNoExist(self):
"""
Test loading model that doesn't exist
"""
# Test non-existent path raises an exception
with self.assertRaises(IOError):
VectorsFactory.create({"path": os.path.join(tempfile.gettempdir(), "noexist")}, None)
def testBlocking(self):
"""
Test blocking load of vector model
"""
config = {"path": self.path}
model = VectorsFactory.create(config, None)
self.assertFalse(model.initialized)
config["ids"] = ["0", "1"]
config["dimensions"] = 10
model = VectorsFactory.create(config, None)
self.assertTrue(model.initialized)
def testTransform(self):
"""
Test word vector transform
"""
model = VectorsFactory.create({"path": self.path}, None)
self.assertEqual(len(model.transform((None, ["txtai"], None))), 10)
def testLookup(self):
"""
Test word vector lookup
"""
model = VectorsFactory.create({"path": self.path}, None)
self.assertEqual(model.lookup(["txtai", "embeddings", "sentence"]).shape, (3, 10))
def setUpClass(cls):
"""
Create single TransformersVectors instance.
"""
cls.model = VectorsFactory.create(
{"method": "transformers", "path": "sentence-transformers/bert-base-nli-mean-tokens", "maxlength": 512}, None
)
def testIndexSerial(self):
"""
Test word vector indexing in single process mode
"""
# Generate data
documents = [(x, "This is a test", None) for x in range(1000)]
model = VectorsFactory.create({"path": self.path, "parallel": False}, None)
ids, dimension, stream = model.index(documents)
self.assertEqual(len(ids), 1000)
self.assertEqual(dimension, 10)
self.assertIsNotNone(os.path.exists(stream))