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