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https://github.com/hwchase17/langchain
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d669b9ece9
### 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>
87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
from typing import List
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from unittest import TestCase
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from unittest.mock import MagicMock, patch
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import requests
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from langchain.docstore.document import Document
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from langchain.document_loaders import CubeSemanticLoader
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class TestCubeSemanticLoader(TestCase):
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@patch.object(requests, "get")
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def test_load_success(self, mock_get: MagicMock) -> None:
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# Arrange
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cube_api_url: str = "https://example.com/cube_api"
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cube_api_token: str = "abc123"
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mock_response: MagicMock = MagicMock()
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mock_response.status_code = 200
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mock_response_json: dict = {
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"cubes": [
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{
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"type": "view",
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"name": "cube1",
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"measures": [{"type": "sum", "name": "sales", "title": "Sales"}],
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"dimensions": [
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{
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"type": "string",
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"name": "product_name",
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"title": "Product Name",
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}
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],
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}
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]
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}
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mock_response.json.return_value = mock_response_json
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mock_get.return_value = mock_response
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expected_docs: List[Document] = [
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Document(
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page_content=(
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"table name: cube1, "
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"column name: sales, "
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"column data type: sum, "
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"column title: Sales, "
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"column description: None"
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),
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metadata={
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"table_name": "cube1",
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"column_name": "sales",
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"column_data_type": "sum",
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"column_title": "Sales",
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"column_description": "None",
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},
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),
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Document(
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page_content=(
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"table name: cube1, "
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"column name: product_name, "
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"column data type: string, "
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"column title: Product Name, "
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"column description: None"
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),
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metadata={
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"table_name": "cube1",
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"column_name": "product_name",
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"column_data_type": "string",
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"column_title": "Product Name",
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"column_description": "None",
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},
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),
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]
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loader: CubeSemanticLoader = CubeSemanticLoader(cube_api_url, cube_api_token)
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# Act
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result: List[Document] = loader.load()
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# Assert
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self.assertEqual(result, expected_docs)
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mock_get.assert_called_once_with(
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cube_api_url,
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headers={
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"Content-Type": "application/json",
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"Authorization": cube_api_token,
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},
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)
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