68c0985482
- Refactored OIDC flow to implement PKCE, state/nonce validation, and BFF pattern. - Centralized configuration in Settings class (DEV_MODE, FRONTEND_URL, OIDC_REDIRECT_URI). - Updated auth routers to use conditional secure cookie flags based on DEV_MODE. - Modernized and cleaned up test suite by removing legacy Streamlit tests. - Fixed linting errors and unused imports across the backend.
83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
import json
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import pytest
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import logging
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from unittest.mock import patch
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from ea_chatbot.graph.workflow import app
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from ea_chatbot.graph.state import AgentState
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from ea_chatbot.utils.logging import get_logger
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from langchain_community.chat_models import FakeListChatModel
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from langchain_core.runnables import RunnableLambda
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@pytest.fixture(autouse=True)
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def reset_logging():
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"""Reset handlers on the root ea_chatbot logger."""
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root = logging.getLogger("ea_chatbot")
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for handler in root.handlers[:]:
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root.removeHandler(handler)
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yield
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for handler in root.handlers[:]:
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root.removeHandler(handler)
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class FakeStructuredModel(FakeListChatModel):
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def with_structured_output(self, schema, **kwargs):
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# Return a runnable that returns a parsed object
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def _invoke(input, config=None, **kwargs):
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content = self.responses[0]
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import json
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data = json.loads(content)
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if hasattr(schema, "model_validate"):
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return schema.model_validate(data)
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return data
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return RunnableLambda(_invoke)
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def test_logging_e2e_json_output(tmp_path):
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"""Test that a full graph run produces structured JSON logs from multiple nodes."""
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log_file = tmp_path / "e2e_test.jsonl"
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# Configure the root logger
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get_logger("ea_chatbot", log_file=str(log_file))
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initial_state: AgentState = {
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"messages": [],
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"question": "Who won in 2024?",
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"analysis": None,
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"next_action": "",
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"plan": None,
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"code": None,
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"code_output": None,
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"error": None,
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"plots": [],
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"dfs": {}
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}
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# Create fake models that support callbacks and structured output
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fake_analyzer_response = """{"data_required": [], "unknowns": [], "ambiguities": ["Which year?"], "conditions": [], "next_action": "clarify"}"""
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fake_analyzer = FakeStructuredModel(responses=[fake_analyzer_response])
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fake_clarify = FakeListChatModel(responses=["Please specify."])
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with patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model") as mock_llm_factory:
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mock_llm_factory.return_value = fake_analyzer
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with patch("ea_chatbot.graph.nodes.clarification.get_llm_model") as mock_clarify_llm_factory:
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mock_clarify_llm_factory.return_value = fake_clarify
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# Run the graph
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list(app.stream(initial_state))
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# Verify file content
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assert log_file.exists()
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lines = log_file.read_text().splitlines()
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assert len(lines) > 0
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# Verify we have logs from different nodes
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node_names = [json.loads(line)["name"] for line in lines]
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assert "ea_chatbot.query_analyzer" in node_names
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assert "ea_chatbot.clarification" in node_names
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# Verify events
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messages = [json.loads(line)["message"] for line in lines]
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assert any("Analyzing question" in m for m in messages)
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assert any("Clarification generated" in m for m in messages)
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