chore: import upstream snapshot with attribution

test/python/testdatabase/testclient.py

@@ -0,0 +1,71 @@
+"""
+Client module tests
+"""
+
+import os
+import time
+import tempfile
+
+from txtai.embeddings import Embeddings
+
+from .testrdbms import Common
+
+
+# pylint: disable=R0904
+class TestClient(Common.TestRDBMS):
+    """
+    Embeddings with content stored in a client RDBMS.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        """
+        Initialize test data.
+        """
+
+        cls.data = [
+            "US tops 5 million confirmed virus cases",
+            "Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg",
+            "Beijing mobilises invasion craft along coast as Taiwan tensions escalate",
+            "The National Park Service warns against sacrificing slower friends in a bear attack",
+            "Maine man wins $1M from $25 lottery ticket",
+            "Make huge profits without work, earn up to $100,000 a day",
+        ]
+
+        # Content backend
+        cls.backend = None
+
+        # Create embeddings model, backed by sentence-transformers & transformers
+        cls.embeddings = Embeddings({"path": "sentence-transformers/nli-mpnet-base-v2"})
+
+    @classmethod
+    def tearDownClass(cls):
+        """
+        Cleanup data.
+        """
+
+        if cls.embeddings:
+            cls.embeddings.close()
+
+    def setUp(self):
+        """
+        Set unique database path for each test.
+        """
+
+        # Generate unique database path and set on embeddings
+        path = os.path.join(tempfile.gettempdir(), f"{int(time.time() * 1000)}.sqlite")
+        self.backend = f"sqlite:///{path}"
+
+        self.embeddings.config["content"] = self.backend
+
+    def testSchema(self):
+        """
+        Test database creation with a specified schema
+        """
+
+        # Default sequence id
+        embeddings = Embeddings(path="sentence-transformers/nli-mpnet-base-v2", content=self.backend, schema="txtai")
+        embeddings.index(self.data)
+
+        result = embeddings.search("feel good story", 1)[0]
+        self.assertEqual(result["text"], self.data[4])