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>
2025-08-09 13:00:34 +00:00 · 2023-07-06 04:18:12 +06:00 · 2023-07-06 04:18:12 +06:00 · d669b9ece9
commit d669b9ece9
parent e533da8bf2
4 changed files with 284 additions and 0 deletions
--- a/docs/extras/modules/data_connection/document_loaders/integrations/cube_semantic.ipynb
+++ b/docs/extras/modules/data_connection/document_loaders/integrations/cube_semantic.ipynb
@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cube Semantic Layer"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook demonstrates the process of retrieving Cube's data model metadata in a format suitable for passing to LLMs as embeddings, thereby enhancing contextual information."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### About Cube"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Cube](https://cube.dev/) is the Semantic Layer for building data apps. It helps data engineers and application developers access data from modern data stores, organize it into consistent definitions, and deliver it to every application."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cube’s data model provides structure and definitions that are used as a context for LLM to understand data and generate correct queries. LLM doesn’t need to navigate complex joins and metrics calculations because Cube abstracts those and provides a simple interface that operates on the business-level terminology, instead of SQL table and column names. This simplification helps LLM to be less error-prone and avoid hallucinations."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Cube Semantic Loader` requires 2 arguments:\n",
+    "| Input Parameter | Description |\n",
+    "| --- | --- |\n",
+    "| `cube_api_url` | The URL of your Cube's deployment REST API. Please refer to the [Cube documentation](https://cube.dev/docs/http-api/rest#configuration-base-path) for more information on configuring the base path. |\n",
+    "| `cube_api_token` | The authentication token generated based on your Cube's API secret. Please refer to the [Cube documentation](https://cube.dev/docs/security#generating-json-web-tokens-jwt) for instructions on generating JSON Web Tokens (JWT). |\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import jwt\n",
+    "from langchain.document_loaders import CubeSemanticLoader\n",
+    "\n",
+    "api_url = \"https://api-example.gcp-us-central1.cubecloudapp.dev/cubejs-api/v1/meta\"\n",
+    "cubejs_api_secret = \"api-secret-here\"\n",
+    "security_context = {}\n",
+    "# Read more about security context here: https://cube.dev/docs/security\n",
+    "api_token = jwt.encode(security_context, cubejs_api_secret, algorithm=\"HS256\")\n",
+    "\n",
+    "loader = CubeSemanticLoader(api_url, api_token)\n",
+    "\n",
+    "documents = loader.load()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Returns:\n",
+    "\n",
+    "A list of documents with the following attributes:\n",
+    "\n",
+    "- `page_content`\n",
+    "- `metadata`\n",
+    "  - `table_name`\n",
+    "  - `column_name`\n",
+    "  - `column_data_type`\n",
+    "  - `column_title`\n",
+    "  - `column_description`"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> page_content='table name: orders_view, column name: orders_view.total_amount, column data type: number, column title: Orders View Total Amount, column description: None' metadata={'table_name': 'orders_view', 'column_name': 'orders_view.total_amount', 'column_data_type': 'number', 'column_title': 'Orders View Total Amount', 'column_description': 'None'}"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/langchain/document_loaders/init.py
+++ b/langchain/document_loaders/init.py
@ -29,6 +29,7 @@ from langchain.document_loaders.college_confidential import CollegeConfidentialL
 from langchain.document_loaders.confluence import ConfluenceLoader
 from langchain.document_loaders.conllu import CoNLLULoader
 from langchain.document_loaders.csv_loader import CSVLoader, UnstructuredCSVLoader
+from langchain.document_loaders.cube_semantic import CubeSemanticLoader
 from langchain.document_loaders.dataframe import DataFrameLoader
 from langchain.document_loaders.diffbot import DiffbotLoader
 from langchain.document_loaders.directory import DirectoryLoader
@ -175,6 +176,7 @@ __all__ = [
    "CoNLLULoader",
    "CollegeConfidentialLoader",
    "ConfluenceLoader",
+    "CubeSemanticLoader",
    "DataFrameLoader",
    "DiffbotLoader",
    "DirectoryLoader",
--- a/langchain/document_loaders/cube_semantic.py
+++ b/langchain/document_loaders/cube_semantic.py
@ -0,0 +1,78 @@
+from typing import List
+
+import requests
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+
+
+class CubeSemanticLoader(BaseLoader):
+    """Load Cube semantic layer metadata."""
+
+    def __init__(
+        self,
+        cube_api_url: str,
+        cube_api_token: str,
+    ):
+        self.cube_api_url = cube_api_url
+        """Use the REST API of your Cube's deployment.
+        Please find out more information here:
+        https://cube.dev/docs/http-api/rest#configuration-base-path
+        """
+        self.cube_api_token = cube_api_token
+        """Authentication tokens are generated based on your Cube's API secret.
+        Please find out more information here:
+        https://cube.dev/docs/security#generating-json-web-tokens-jwt
+        """
+
+    def load(self) -> List[Document]:
+        """Makes a call to Cube's REST API metadata endpoint.
+
+        Returns:
+            A list of documents with attributes:
+                - page_content=column_name
+                - metadata
+                    - table_name
+                    - column_name
+                    - column_data_type
+                    - column_title
+                    - column_description
+        """
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": self.cube_api_token,
+        }
+
+        response = requests.get(self.cube_api_url, headers=headers)
+        response.raise_for_status()
+        raw_meta_json = response.json()
+        cubes = raw_meta_json.get("cubes", [])
+        docs = []
+
+        for cube in cubes:
+            if cube.get("type") != "view":
+                continue
+
+            cube_name = cube.get("name")
+
+            measures = cube.get("measures", [])
+            dimensions = cube.get("dimensions", [])
+
+            for item in measures + dimensions:
+                metadata = dict(
+                    table_name=str(cube_name),
+                    column_name=str(item.get("name")),
+                    column_data_type=str(item.get("type")),
+                    column_title=str(item.get("title")),
+                    column_description=str(item.get("description")),
+                )
+
+                page_content = f"table name: {str(cube_name)}, "
+                page_content += f"column name: {str(item.get('name'))}, "
+                page_content += f"column data type: {str(item.get('type'))}, "
+                page_content += f"column title: {str(item.get('title'))}, "
+                page_content += f"column description: {str(item.get('description'))}"
+
+                docs.append(Document(page_content=page_content, metadata=metadata))
+
+        return docs
--- a/tests/unit_tests/document_loaders/test_cube_semantic.py
+++ b/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,
+            },
+        )