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.
103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
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
|
|
|
|
@pytest.fixture
|
|
def base_state():
|
|
return {
|
|
"messages": [],
|
|
"question": "",
|
|
"analysis": None,
|
|
"next_action": "",
|
|
"summary": ""
|
|
}
|
|
|
|
@patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model")
|
|
def test_refinement_coreference_from_history(mock_get_llm, base_state):
|
|
"""
|
|
Test that the analyzer can resolve Year/State from history.
|
|
User asks "What about in NJ?" after a Florida 2024 query.
|
|
Expected: next_action = 'plan', NOT 'clarify' due to missing year.
|
|
"""
|
|
state = base_state.copy()
|
|
state["messages"] = [
|
|
HumanMessage(content="Show me 2024 results for Florida"),
|
|
AIMessage(content="Here are the 2024 results for Florida...")
|
|
]
|
|
state["question"] = "What about in New Jersey?"
|
|
state["summary"] = "The user is looking for 2024 election results."
|
|
|
|
mock_llm = MagicMock()
|
|
mock_get_llm.return_value = mock_llm
|
|
mock_structured = MagicMock()
|
|
mock_llm.with_structured_output.return_value = mock_structured
|
|
|
|
# We expect the LLM to eventually return 'plan' because it sees the context.
|
|
# For now, if it returns 'clarify', this test should fail once we update the prompt to BE less strict.
|
|
mock_structured.invoke.return_value = QueryAnalysis(
|
|
data_required=["2024 results", "New Jersey"],
|
|
unknowns=[],
|
|
ambiguities=[],
|
|
conditions=["state=NJ", "year=2024"],
|
|
next_action="plan"
|
|
)
|
|
|
|
result = query_analyzer_node(state)
|
|
assert result["next_action"] == "plan"
|
|
assert "NJ" in str(result["analysis"]["conditions"])
|
|
|
|
@patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model")
|
|
def test_refinement_tolerance_for_missing_format(mock_get_llm, base_state):
|
|
"""
|
|
Test that the analyzer doesn't flag missing output format or database name.
|
|
User asks "Give me a graph of turnout".
|
|
Expected: next_action = 'plan', even if 'format' or 'db' is not in query.
|
|
"""
|
|
state = base_state.copy()
|
|
state["question"] = "Give me a graph of voter turnout in 2024 for Florida"
|
|
|
|
mock_llm = MagicMock()
|
|
mock_get_llm.return_value = mock_llm
|
|
mock_structured = MagicMock()
|
|
mock_llm.with_structured_output.return_value = mock_structured
|
|
|
|
mock_structured.invoke.return_value = QueryAnalysis(
|
|
data_required=["voter turnout", "Florida"],
|
|
unknowns=[],
|
|
ambiguities=[],
|
|
conditions=["year=2024"],
|
|
next_action="plan"
|
|
)
|
|
|
|
result = query_analyzer_node(state)
|
|
assert result["next_action"] == "plan"
|
|
# Ensure no ambiguities were added by the analyzer itself (hallucinated requirement)
|
|
assert len(result["analysis"]["ambiguities"]) == 0
|
|
|
|
@patch("ea_chatbot.graph.nodes.query_analyzer.get_llm_model")
|
|
def test_refinement_enforces_voter_identity_clarification(mock_get_llm, base_state):
|
|
"""
|
|
Test that 'track the same voter' still triggers clarification.
|
|
"""
|
|
state = base_state.copy()
|
|
state["question"] = "Track the same voter participation in 2020 and 2024."
|
|
|
|
mock_llm = MagicMock()
|
|
mock_get_llm.return_value = mock_llm
|
|
mock_structured = MagicMock()
|
|
mock_llm.with_structured_output.return_value = mock_structured
|
|
|
|
# We WANT it to clarify here because voter identity is not defined.
|
|
mock_structured.invoke.return_value = QueryAnalysis(
|
|
data_required=["voter participation"],
|
|
unknowns=[],
|
|
ambiguities=["Please define what fields constitute 'the same voter' (e.g. ID, or Name and DOB)."],
|
|
conditions=[],
|
|
next_action="clarify"
|
|
)
|
|
|
|
result = query_analyzer_node(state)
|
|
assert result["next_action"] == "clarify"
|
|
assert "identity" in str(result["analysis"]["ambiguities"]).lower() or "same voter" in str(result["analysis"]["ambiguities"]).lower()
|