chore: import upstream snapshot with attribution

tests/test_csv_scraper_multi_graph.py

@@ -0,0 +1,161 @@
+from copy import deepcopy
+
+import pytest
+
+from scrapegraphai.graphs.csv_scraper_multi_graph import CSVScraperMultiGraph
+
+# Monkey-patch _create_llm to avoid unsupported provider error during tests
+CSVScraperMultiGraph._create_llm = lambda self, llm_config: llm_config
+
+
+# Dummy graph classes to simulate behavior during tests
+class DummyGraph:
+    """Dummy graph that returns a predefined answer."""
+
+    def __init__(self, answer):
+        self.answer = answer
+
+    def execute(self, inputs):
+        # Returns a tuple of (final_state, execution_info)
+        return ({"answer": self.answer}, {})
+
+
+class DummyGraphNoAnswer:
+    """Dummy graph that simulates absence of answer in final_state."""
+
+    def execute(self, inputs):
+        # Returns an empty final_state
+        return ({}, {})
+
+
+class DummyBaseGraph:
+    """Dummy BaseGraph to test _create_graph method without side effects."""
+
+    def __init__(self, nodes, edges, entry_point, graph_name):
+        self.nodes = nodes
+        self.edges = edges
+        self.entry_point = entry_point
+        self.graph_name = graph_name
+
+    config = {
+        "llm": {"model": "dummy_model", "model_provider": "dummy_provider"},
+        "key": "value",
+    }
+    """Test that CSVScraperMultiGraph.run returns the expected answer when provided by the graph."""
+    prompt = "Test prompt"
+    source = ["url1", "url2"]
+
+    # Instantiate the graph
+    multi_graph = CSVScraperMultiGraph(prompt, source, config)
+
+    # Override the graph attribute with a dummy graph returning an expected answer
+    multi_graph.graph = DummyGraph("expected answer")
+
+    result = multi_graph.run()
+    assert result == "expected answer"
+
+
+def test_run_no_answer():
+    """Test that CSVScraperMultiGraph.run returns a fallback message when no answer is provided."""
+    prompt = "Another test prompt"
+    source = ["url3"]
+    config = {
+        "llm": {"model": "dummy_model", "model_provider": "dummy_provider"},
+        "another_key": "another_value",
+    }
+
+    multi_graph = CSVScraperMultiGraph(prompt, source, config)
+    multi_graph.graph = DummyGraphNoAnswer()
+
+    result = multi_graph.run()
+    assert result == "No answer found."
+
+
+def test_create_graph_structure(monkeypatch):
+    """Test that _create_graph constructs a graph with the expected structure."""
+    prompt = "Structure test"
+    source = ["url4"]
+    config = {
+        "llm": {"model": "dummy_model", "model_provider": "dummy_provider"},
+        "struct_key": "struct_value",
+    }
+
+    multi_graph = CSVScraperMultiGraph(prompt, source, config)
+
+    # Monkey-patch the _create_graph method to avoid dependencies on external nodes
+    monkeypatch.setattr(
+        multi_graph,
+        "_create_graph",
+        lambda: DummyBaseGraph(
+            nodes=["graph_iterator_node", "merge_answers_node"],
+            edges=[("graph_iterator_node", "merge_answers_node")],
+            entry_point="graph_iterator_node",
+            graph_name=multi_graph.__class__.__name__,
+        ),
+    )
+
+    graph = multi_graph._create_graph()
+    assert graph.graph_name == "CSVScraperMultiGraph"
+    assert len(graph.nodes) == 2
+    assert len(graph.edges) == 1
+
+
+def test_config_deepcopy():
+    """Test that the configuration dictionary is deep-copied.
+    Modifying the original config after instantiation should not affect the multi_graph copy.
+    """
+    config = {
+        "llm": {"model": "dummy_model", "provider": "provider1"},
+        "nested": {"a": [1, 2]},
+    }
+    original_config = deepcopy(config)
+    multi_graph = CSVScraperMultiGraph("Deep copy test", ["url_deep"], config)
+    # Modify the original config after instantiation
+    config["nested"]["a"].append(3)
+    # The multi_graph.copy_config should remain unchanged.
+    assert multi_graph.copy_config["nested"]["a"] == original_config["nested"]["a"]
+
+
+def test_run_argument_passing():
+    """Test that CSVScraperMultiGraph.run passes the correct input arguments
+    to the graph's execute method and returns the expected answer."""
+
+    class DummyGraphCapture:
+        def __init__(self):
+            self.captured_inputs = None
+
+        def execute(self, inputs):
+            self.captured_inputs = inputs
+            return ({"answer": "captured answer"}, {})
+
+    prompt = "Argument test prompt"
+    source = ["url_arg1", "url_arg2"]
+    config = {"llm": {"model": "dummy_model", "provider": "dummy_provider"}}
+
+    multi_graph = CSVScraperMultiGraph(prompt, source, config)
+    dummy_graph = DummyGraphCapture()
+    multi_graph.graph = dummy_graph
+
+    result = multi_graph.run()
+    # Check that the dummy graph captured the inputs as expected
+    expected_inputs = {"user_prompt": prompt, "jsons": source}
+    assert dummy_graph.captured_inputs == expected_inputs
+    assert result == "captured answer"
+
+
+def test_run_with_exception_in_execute():
+    """Test that CSVScraperMultiGraph.run propagates exceptions from the graph's execute method."""
+
+    class DummyGraphException:
+        def execute(self, inputs):
+            raise Exception("Test exception")
+
+    prompt = "Exception test prompt"
+    source = ["url_exception"]
+    config = {"llm": {"model": "dummy_model", "provider": "dummy_provider"}}
+
+    multi_graph = CSVScraperMultiGraph(prompt, source, config)
+    multi_graph.graph = DummyGraphException()
+
+    with pytest.raises(Exception, match="Test exception"):
+        multi_graph.run()