Document loader for Cube Semantic Layer (#6882)

### Description

This pull request introduces the "Cube Semantic Layer" document loader,
which demonstrates the retrieval of Cube's data model metadata in a
format suitable for passing to LLMs as embeddings. This enhancement aims
to provide contextual information and improve the understanding of data.

Twitter handle:
@the_cube_dev

---------

Co-authored-by: rlm <pexpresss31@gmail.com>

tests/unit_tests/document_loaders/test_cube_semantic.py

@@ -0,0 +1,86 @@
+from typing import List
+from unittest import TestCase
+from unittest.mock import MagicMock, patch
+
+import requests
+
+from langchain.docstore.document import Document
+from langchain.document_loaders import CubeSemanticLoader
+
+
+class TestCubeSemanticLoader(TestCase):
+    @patch.object(requests, "get")
+    def test_load_success(self, mock_get: MagicMock) -> None:
+        # Arrange
+        cube_api_url: str = "https://example.com/cube_api"
+        cube_api_token: str = "abc123"
+        mock_response: MagicMock = MagicMock()
+        mock_response.status_code = 200
+        mock_response_json: dict = {
+            "cubes": [
+                {
+                    "type": "view",
+                    "name": "cube1",
+                    "measures": [{"type": "sum", "name": "sales", "title": "Sales"}],
+                    "dimensions": [
+                        {
+                            "type": "string",
+                            "name": "product_name",
+                            "title": "Product Name",
+                        }
+                    ],
+                }
+            ]
+        }
+        mock_response.json.return_value = mock_response_json
+        mock_get.return_value = mock_response
+
+        expected_docs: List[Document] = [
+            Document(
+                page_content=(
+                    "table name: cube1, "
+                    "column name: sales, "
+                    "column data type: sum, "
+                    "column title: Sales, "
+                    "column description: None"
+                ),
+                metadata={
+                    "table_name": "cube1",
+                    "column_name": "sales",
+                    "column_data_type": "sum",
+                    "column_title": "Sales",
+                    "column_description": "None",
+                },
+            ),
+            Document(
+                page_content=(
+                    "table name: cube1, "
+                    "column name: product_name, "
+                    "column data type: string, "
+                    "column title: Product Name, "
+                    "column description: None"
+                ),
+                metadata={
+                    "table_name": "cube1",
+                    "column_name": "product_name",
+                    "column_data_type": "string",
+                    "column_title": "Product Name",
+                    "column_description": "None",
+                },
+            ),
+        ]
+
+        loader: CubeSemanticLoader = CubeSemanticLoader(cube_api_url, cube_api_token)
+
+        # Act
+        result: List[Document] = loader.load()
+
+        # Assert
+        self.assertEqual(result, expected_docs)
+        mock_get.assert_called_once_with(
+            cube_api_url,
+            headers={
+                "Content-Type": "application/json",
+                "Authorization": cube_api_token,
+            },
+        )