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

tests/test_logging_e2e.py

@@ -0,0 +1,83 @@
+import os
+import json
+import pytest
+import logging
+from unittest.mock import MagicMock, patch
+from ea_chatbot.graph.workflow import app
+from ea_chatbot.graph.state import AgentState
+from ea_chatbot.utils.logging import get_logger
+from langchain_community.chat_models import FakeListChatModel
+from langchain_core.runnables import RunnableLambda
+
+@pytest.fixture(autouse=True)
+def reset_logging():
+    """Reset handlers on the root ea_chatbot logger."""
+    root = logging.getLogger("ea_chatbot")
+    for handler in root.handlers[:]:
+        root.removeHandler(handler)
+    yield
+    for handler in root.handlers[:]:
+        root.removeHandler(handler)
+
+class FakeStructuredModel(FakeListChatModel):
+    def with_structured_output(self, schema, **kwargs):
+        # Return a runnable that returns a parsed object
+        def _invoke(input, config=None, **kwargs):
+            content = self.responses[0]
+            import json
+            data = json.loads(content)
+            if hasattr(schema, "model_validate"):
+                return schema.model_validate(data)
+            return data
+            
+        return RunnableLambda(_invoke)
+
+def test_logging_e2e_json_output(tmp_path):
+    """Test that a full graph run produces structured JSON logs from multiple nodes."""
+    log_file = tmp_path / "e2e_test.jsonl"
+    
+    # Configure the root logger
+    get_logger("ea_chatbot", log_file=str(log_file))
+    
+    initial_state: AgentState = {
+        "messages": [],
+        "question": "Who won in 2024?",
+        "analysis": None,
+        "next_action": "",
+        "plan": None,
+        "code": None,
+        "code_output": None,
+        "error": None,
+        "plots": [],
+        "dfs": {}
+    }
+    
+    # Create fake models that support callbacks and structured output
+    fake_analyzer_response = """{"data_required": [], "unknowns": [], "ambiguities": ["Which year?"], "conditions": [], "next_action": "clarify"}"""
+    fake_analyzer = FakeStructuredModel(responses=[fake_analyzer_response])
+    
+    fake_clarify = FakeListChatModel(responses=["Please specify."])
+    
+    with patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model") as mock_llm_factory:
+        mock_llm_factory.return_value = fake_analyzer
+        
+        with patch("ea_chatbot.graph.nodes.clarification.get_llm_model") as mock_clarify_llm_factory:
+            mock_clarify_llm_factory.return_value = fake_clarify
+            
+            # Run the graph
+            list(app.stream(initial_state))
+        
+    # Verify file content
+    assert log_file.exists()
+    lines = log_file.read_text().splitlines()
+    assert len(lines) > 0
+    
+    # Verify we have logs from different nodes
+    node_names = [json.loads(line)["name"] for line in lines]
+    assert "ea_chatbot.query_analyzer" in node_names
+    assert "ea_chatbot.clarification" in node_names
+    
+    # Verify events
+    messages = [json.loads(line)["message"] for line in lines]
+    assert any("Analyzing question" in m for m in messages)
+    assert any("Clarification generated" in m for m in messages)