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

tests/test_conversation_summary.py

@@ -0,0 +1,56 @@
+import pytest
+from unittest.mock import MagicMock, patch
+from langchain_core.messages import HumanMessage, AIMessage
+from ea_chatbot.graph.nodes.summarize_conversation import summarize_conversation_node
+
+@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...")
+        ],
+        "summary": "The user is asking about 2024 election results."
+    }
+
+@patch("ea_chatbot.graph.nodes.summarize_conversation.get_llm_model")
+def test_summarize_conversation_node_updates_summary(mock_get_llm, mock_state_with_history):
+    mock_llm_instance = MagicMock()
+    mock_get_llm.return_value = mock_llm_instance
+    
+    # Mock LLM response for updating summary
+    mock_llm_instance.invoke.return_value = AIMessage(content="Updated summary including NJ results.")
+    
+    # Add new messages to simulate a completed turn
+    mock_state_with_history["messages"].extend([
+        HumanMessage(content="What about in New Jersey?"),
+        AIMessage(content="In New Jersey, the 2024 results were...")
+    ])
+    
+    result = summarize_conversation_node(mock_state_with_history)
+    
+    assert "summary" in result
+    assert result["summary"] == "Updated summary including NJ results."
+    
+    # Verify LLM was called with the correct context
+    call_messages = mock_llm_instance.invoke.call_args[0][0]
+    # Should include current summary and last turn messages
+    assert "Current summary: The user is asking about 2024 election results." in call_messages[0].content
+
+@patch("ea_chatbot.graph.nodes.summarize_conversation.get_llm_model")
+def test_summarize_conversation_node_initial_summary(mock_get_llm):
+    state = {
+        "messages": [
+            HumanMessage(content="Hi"),
+            AIMessage(content="Hello! How can I help you today?")
+        ],
+        "summary": ""
+    }
+    
+    mock_llm_instance = MagicMock()
+    mock_get_llm.return_value = mock_llm_instance
+    mock_llm_instance.invoke.return_value = AIMessage(content="Initial greeting.")
+    
+    result = summarize_conversation_node(state)
+    
+    assert result["summary"] == "Initial greeting."