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

tests/test_multi_turn_query_analyzer.py

@@ -0,0 +1,76 @@
+import pytest
+from unittest.mock import MagicMock, patch
+from langchain_core.messages import HumanMessage, AIMessage
+from ea_chatbot.graph.nodes.query_analyzer import query_analyzer_node, QueryAnalysis
+from ea_chatbot.graph.state import AgentState
+
+@pytest.fixture
+def mock_state_with_history():
+    return {
+        "messages": [
+            HumanMessage(content="Show me the 2024 results for Florida"),
+            AIMessage(content="Here are the results for Florida in 2024...")
+        ],
+        "question": "What about in New Jersey?",
+        "analysis": None,
+        "next_action": "",
+        "summary": "The user is asking about 2024 election results."
+    }
+
+@patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model")
+@patch("ea_chatbot.graph.nodes.query_analyzer.QUERY_ANALYZER_PROMPT")
+def test_query_analyzer_uses_history_and_summary(mock_prompt, mock_get_llm, mock_state_with_history):
+    """Test that query_analyzer_node passes history and summary to the prompt."""
+    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=["2024 results", "New Jersey"],
+        unknowns=[],
+        ambiguities=[],
+        conditions=[],
+        next_action="plan"
+    )
+    
+    query_analyzer_node(mock_state_with_history)
+    
+    # Verify that the prompt was formatted with the correct variables
+    mock_prompt.format_messages.assert_called_once()
+    kwargs = mock_prompt.format_messages.call_args[1]
+    
+    assert kwargs["question"] == "What about in New Jersey?"
+    assert "summary" in kwargs
+    assert kwargs["summary"] == mock_state_with_history["summary"]
+    assert "history" in kwargs
+    # History should contain the messages from the state
+    assert len(kwargs["history"]) == 2
+    assert kwargs["history"][0].content == "Show me the 2024 results for Florida"
+
+@patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model")
+def test_query_analyzer_context_window(mock_get_llm):
+    """Test that query_analyzer_node only uses the last 6 messages (3 turns)."""
+    messages = [HumanMessage(content=f"Msg {i}") for i in range(10)]
+    state = {
+        "messages": messages,
+        "question": "Latest question",
+        "analysis": None,
+        "next_action": "",
+        "summary": "Summary"
+    }
+    
+    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"
+    )
+
+    with patch("ea_chatbot.graph.nodes.query_analyzer.QUERY_ANALYZER_PROMPT") as mock_prompt:
+        query_analyzer_node(state)
+        kwargs = mock_prompt.format_messages.call_args[1]
+        # Should only have last 6 messages
+        assert len(kwargs["history"]) == 6
+        assert kwargs["history"][0].content == "Msg 4"