feat: implement mvp with email-first login flow and langgraph architecture

tests/test_query_analyzer.py

@@ -0,0 +1,80 @@
+import pytest
+from unittest.mock import MagicMock, patch
+from ea_chatbot.graph.nodes.query_analyzer import query_analyzer_node, QueryAnalysis
+from ea_chatbot.graph.state import AgentState
+
+@pytest.fixture
+def mock_state():
+    return {
+        "messages": [],
+        "question": "Show me the 2024 results for Florida",
+        "analysis": None,
+        "next_action": ""
+    }
+
+@patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model")
+def test_query_analyzer_data_analysis(mock_get_llm, mock_state):
+    """Test that a clear data analysis query is routed to the planner."""
+    # Mock the LLM and the structured output runnable
+    mock_llm_instance = MagicMock()
+    mock_get_llm.return_value = mock_llm_instance
+    mock_structured_llm = MagicMock()
+    mock_llm_instance.with_structured_output.return_value = mock_structured_llm
+    # Define the expected Pydantic result
+    expected_analysis = QueryAnalysis(
+        data_required=["2024 results", "Florida"],
+        unknowns=[],
+        ambiguities=[],
+        conditions=[],
+        next_action="plan"
+    )
+    # When structured_llm.invoke is called with messages, return the Pydantic object
+    mock_structured_llm.invoke.return_value = expected_analysis
+    
+    new_state_update = query_analyzer_node(mock_state)
+    
+    assert new_state_update["next_action"] == "plan"
+    assert "2024 results" in new_state_update["analysis"]["data_required"]
+
+@patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model")
+def test_query_analyzer_ambiguous(mock_get_llm, mock_state):
+    """Test that an ambiguous query is routed to clarification."""
+    mock_state["question"] = "What happened?"
+    
+    mock_llm_instance = MagicMock()
+    mock_get_llm.return_value = mock_llm_instance
+    mock_structured_llm = MagicMock()
+    mock_llm_instance.with_structured_output.return_value = mock_structured_llm
+    expected_analysis = QueryAnalysis(
+        data_required=[],
+        unknowns=["What event?"],
+        ambiguities=[],
+        conditions=[],
+        next_action="clarify"
+    )
+    
+    mock_structured_llm.invoke.return_value = expected_analysis
+    
+    new_state_update = query_analyzer_node(mock_state)
+    
+    assert new_state_update["next_action"] == "clarify"
+    assert len(new_state_update["analysis"]["unknowns"]) > 0
+
+@patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model")
+def test_query_analyzer_uses_config(mock_get_llm, mock_state, monkeypatch):
+    """Test that the node uses the configured LLM settings."""
+    monkeypatch.setenv("QUERY_ANALYZER_LLM__MODEL", "gpt-3.5-turbo")
+    
+    mock_llm_instance = MagicMock()
+    mock_get_llm.return_value = mock_llm_instance
+    mock_structured_llm = MagicMock()
+    mock_llm_instance.with_structured_output.return_value = mock_structured_llm
+    mock_structured_llm.invoke.return_value = QueryAnalysis(
+        data_required=[], unknowns=[], ambiguities=[], conditions=[], next_action="plan"
+    )
+    
+    query_analyzer_node(mock_state)
+    
+    # Verify get_llm_model was called with the overridden config
+    called_config = mock_get_llm.call_args[0][0]
+    assert called_config.model == "gpt-3.5-turbo"