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test: Enhance JSON scraping pipeline test
This commit enhances the test suite for the JSON scraping pipeline by introducing the following improvements: - Separate configuration from the test code by loading it from a JSON file (config.json) - Use a parametrized fixture to run the test with multiple configurations automatically - Read the sample JSON file from a separate inputs directory for better organization - Add explicit assertions to verify the expected output (list of titles) - Improve test organization and separation of concerns using fixtures - Promote better coding practices and make the test suite more extensible These changes aim to improve the testability, maintainability, and flexibility of the test suite. They make it easier to manage configurations, add or modify test cases, and ensure the robustness of the scraping pipeline. The test suite now follows best practices and is better prepared for future changes and requirements.
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tests/graphs/scrape_json_ollama.py

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"""
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Module for scraping json documents
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"""
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Module for scraping JSON documents
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"""
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import os
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import json
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import pytest
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from scrapegraphai.graphs import JSONScraperGraph
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# Load configuration from a JSON file
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CONFIG_FILE = "config.json"
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with open(CONFIG_FILE, "r") as f:
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CONFIG = json.load(f)
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# Fixture to read the sample JSON file
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@pytest.fixture
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def sample_json():
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"""
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Example of text
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Read the sample JSON file
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"""
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file_name = "inputs/example.json"
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curr_dir = os.path.dirname(os.path.realpath(__file__))
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file_path = os.path.join(curr_dir, file_name)
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with open(file_path, 'r', encoding="utf-8") as file:
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file_path = os.path.join(os.path.dirname(__file__), "inputs", "example.json")
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with open(file_path, "r", encoding="utf-8") as file:
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text = file.read()
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return text
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@pytest.fixture
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def graph_config():
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# Parametrized fixture to load graph configurations
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@pytest.fixture(params=CONFIG["graph_configs"])
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def graph_config(request):
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"""
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Configuration of the graph
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Load graph configuration
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"""
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return {
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"llm": {
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"model": "ollama/mistral",
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"temperature": 0,
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"format": "json",
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"base_url": "http://localhost:11434",
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},
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"embeddings": {
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"model": "ollama/nomic-embed-text",
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"temperature": 0,
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"base_url": "http://localhost:11434",
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}
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}
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return request.param
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def test_scraping_pipeline(sample_json: str, graph_config: dict):
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# Test function for the scraping pipeline
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def test_scraping_pipeline(sample_json, graph_config):
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"""
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Start of the scraping pipeline
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Test the scraping pipeline
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"""
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expected_titles = ["Title 1", "Title 2", "Title 3"] # Replace with expected titles
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smart_scraper_graph = JSONScraperGraph(
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prompt="List me all the titles",
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source=sample_json,
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config=graph_config
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)
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result = smart_scraper_graph.run()
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assert result is not None
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assert isinstance(result, list)
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assert sorted(result) == sorted(expected_titles)

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