TxtAI MCP Server

""" 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"})