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Unit tests for the escalation engine module.
Tests escalation decision rules including threshold comparisons
and confidence explanation generation.
**Validates: Requirements 4.1, 4.2, 4.3, 4.4, 4.5**
"""
import pytest
from unittest.mock import Mock, MagicMock
from src.escalation import EscalationEngine, EscalationDecision
from src.rag import RetrievedChunk
from src.llm_client import LLMClient, LLMResponse
class TestEscalationDecisionRules:
"""Test cases for escalation decision logic."""
@pytest.fixture
def mock_llm_client(self):
"""Create a mock LLM client."""
return Mock(spec=LLMClient)
@pytest.fixture
def engine(self, mock_llm_client):
"""Create escalation engine with default threshold."""
return EscalationEngine(
llm_client=mock_llm_client,
relevance_threshold=0.5
)
def test_escalate_on_empty_chunks(self, engine):
"""Should escalate when no chunks are retrieved."""
decision = engine.process_question("test question", [])
assert decision.action == "escalation"
assert "no relevant information" in decision.confidence_explanation.lower()
def test_escalate_on_low_relevance_score(self, engine):
"""Should escalate when best relevance score is below threshold."""
chunks = [
RetrievedChunk(
content="Some content",
score=0.3, # Below 0.5 threshold
source="test.md",
chunk_id=0,
position=0
)
]
decision = engine.process_question("test question", chunks)
assert decision.action == "escalation"
assert "0.30" in decision.confidence_explanation or "0.3" in decision.confidence_explanation
assert decision.sources is None
def test_escalate_on_llm_uncertainty(self, engine, mock_llm_client):
"""Should escalate when LLM signals uncertainty."""
chunks = [
RetrievedChunk(
content="Relevant content",
score=0.8, # High score
source="test.md",
chunk_id=0,
position=0
)
]
# Mock LLM to return uncertain response
mock_llm_client.generate_answer.return_value = LLMResponse(
answer="I'm not certain about this.",
uncertain=True
)
decision = engine.process_question("test question", chunks)
assert decision.action == "escalation"
assert "uncertainty" in decision.confidence_explanation.lower()
assert decision.sources is None
def test_answer_on_high_relevance(self, engine, mock_llm_client):
"""Should answer when relevance score is above threshold and LLM is confident."""
chunks = [
RetrievedChunk(
content="Relevant content",
score=0.8,
source="billing.md",
chunk_id=0,
position=0
)
]
# Mock LLM to return confident answer
mock_llm_client.generate_answer.return_value = LLMResponse(
answer="Here is the answer to your question.",
uncertain=False
)
decision = engine.process_question("test question", chunks)
assert decision.action == "answer"
assert decision.message == "Here is the answer to your question."
assert "0.8" in decision.confidence_explanation or "0.80" in decision.confidence_explanation
assert decision.sources == ["billing.md"]
def test_threshold_boundary_below(self, engine):
"""Should escalate when score is exactly at threshold (0.5)."""
chunks = [
RetrievedChunk(
content="Content",
score=0.49, # Just below threshold
source="test.md",
chunk_id=0,
position=0
)
]
decision = engine.process_question("test question", chunks)
assert decision.action == "escalation"
def test_threshold_boundary_above(self, engine, mock_llm_client):
"""Should attempt to answer when score is above threshold."""
chunks = [
RetrievedChunk(
content="Content",
score=0.51, # Just above threshold
source="test.md",
chunk_id=0,
position=0
)
]
mock_llm_client.generate_answer.return_value = LLMResponse(
answer="Answer",
uncertain=False
)
decision = engine.process_question("test question", chunks)
assert decision.action == "answer"
def test_custom_threshold(self, mock_llm_client):
"""Should respect custom relevance threshold."""
engine = EscalationEngine(
llm_client=mock_llm_client,
relevance_threshold=0.7 # Higher threshold
)
chunks = [
RetrievedChunk(
content="Content",
score=0.6, # Would pass 0.5 but not 0.7
source="test.md",
chunk_id=0,
position=0
)
]
decision = engine.process_question("test question", chunks)
assert decision.action == "escalation"
assert "0.7" in decision.confidence_explanation or "0.70" in decision.confidence_explanation
class TestConfidenceExplanationGeneration:
"""Test cases for confidence explanation generation."""
@pytest.fixture
def mock_llm_client(self):
"""Create a mock LLM client."""
return Mock(spec=LLMClient)
@pytest.fixture
def engine(self, mock_llm_client):
"""Create escalation engine."""
return EscalationEngine(llm_client=mock_llm_client)
def test_explanation_includes_score_for_answer(self, engine, mock_llm_client):
"""Confidence explanation for answers should include relevance score."""
chunks = [
RetrievedChunk(
content="Content",
score=0.85,
source="test.md",
chunk_id=0,
position=0
)
]
mock_llm_client.generate_answer.return_value = LLMResponse(
answer="Answer",
uncertain=False
)
decision = engine.process_question("test", chunks)
assert "0.85" in decision.confidence_explanation
assert "confidence" in decision.confidence_explanation.lower()
def test_explanation_includes_source_for_answer(self, engine, mock_llm_client):
"""Confidence explanation should mention source article."""
chunks = [
RetrievedChunk(
content="Content",
score=0.8,
source="billing.md",
chunk_id=0,
position=0
)
]
mock_llm_client.generate_answer.return_value = LLMResponse(
answer="Answer",
uncertain=False
)
decision = engine.process_question("test", chunks)
assert "billing.md" in decision.confidence_explanation
def test_explanation_for_multiple_sources(self, engine, mock_llm_client):
"""Should mention multiple sources in explanation."""
chunks = [
RetrievedChunk(content="C1", score=0.8, source="billing.md", chunk_id=0, position=0),
RetrievedChunk(content="C2", score=0.7, source="permissions.md", chunk_id=1, position=0)
]
mock_llm_client.generate_answer.return_value = LLMResponse(
answer="Answer",
uncertain=False
)
decision = engine.process_question("test", chunks)
# Should mention multiple articles
assert "2" in decision.confidence_explanation or "multiple" in decision.confidence_explanation.lower()
def test_explanation_for_low_score_escalation(self, engine):
"""Escalation explanation should mention low score."""
chunks = [
RetrievedChunk(
content="Content",
score=0.3,
source="test.md",
chunk_id=0,
position=0
)
]
decision = engine.process_question("test", chunks)
assert decision.action == "escalation"
assert "0.3" in decision.confidence_explanation or "0.30" in decision.confidence_explanation
assert "threshold" in decision.confidence_explanation.lower()
def test_explanation_for_llm_uncertainty(self, engine, mock_llm_client):
"""Escalation explanation should mention LLM uncertainty."""
chunks = [
RetrievedChunk(content="Content", score=0.8, source="test.md", chunk_id=0, position=0)
]
mock_llm_client.generate_answer.return_value = LLMResponse(
answer="Not sure",
uncertain=True
)
decision = engine.process_question("test", chunks)
assert decision.action == "escalation"
assert "uncertainty" in decision.confidence_explanation.lower()
def test_explanation_for_no_chunks(self, engine):
"""Escalation explanation should mention no relevant information."""
decision = engine.process_question("test", [])
assert decision.action == "escalation"
assert "no relevant" in decision.confidence_explanation.lower()
class TestSourceExtraction:
"""Test cases for source reference extraction."""
@pytest.fixture
def mock_llm_client(self):
"""Create a mock LLM client."""
return Mock(spec=LLMClient)
@pytest.fixture
def engine(self, mock_llm_client):
"""Create escalation engine."""
return EscalationEngine(llm_client=mock_llm_client)
def test_sources_included_for_answers(self, engine, mock_llm_client):
"""Answer responses should include source references."""
chunks = [
RetrievedChunk(content="C", score=0.8, source="billing.md", chunk_id=0, position=0)
]
mock_llm_client.generate_answer.return_value = LLMResponse(
answer="Answer",
uncertain=False
)
decision = engine.process_question("test", chunks)
assert decision.sources is not None
assert "billing.md" in decision.sources
def test_sources_not_included_for_escalations(self, engine):
"""Escalation responses should not include sources."""
chunks = [
RetrievedChunk(content="C", score=0.2, source="test.md", chunk_id=0, position=0)
]
decision = engine.process_question("test", chunks)
assert decision.action == "escalation"
assert decision.sources is None
def test_multiple_sources_deduplicated(self, engine, mock_llm_client):
"""Should deduplicate sources from multiple chunks."""
chunks = [
RetrievedChunk(content="C1", score=0.8, source="billing.md", chunk_id=0, position=0),
RetrievedChunk(content="C2", score=0.7, source="billing.md", chunk_id=1, position=100),
RetrievedChunk(content="C3", score=0.6, source="permissions.md", chunk_id=0, position=0)
]
mock_llm_client.generate_answer.return_value = LLMResponse(
answer="Answer",
uncertain=False
)
decision = engine.process_question("test", chunks)
assert len(decision.sources) == 2
assert "billing.md" in decision.sources
assert "permissions.md" in decision.sources
def test_sources_sorted(self, engine, mock_llm_client):
"""Sources should be sorted alphabetically."""
chunks = [
RetrievedChunk(content="C1", score=0.8, source="permissions.md", chunk_id=0, position=0),
RetrievedChunk(content="C2", score=0.7, source="billing.md", chunk_id=0, position=0)
]
mock_llm_client.generate_answer.return_value = LLMResponse(
answer="Answer",
uncertain=False
)
decision = engine.process_question("test", chunks)
assert decision.sources == ["billing.md", "permissions.md"]
class TestErrorHandling:
"""Test cases for error handling in escalation engine."""
@pytest.fixture
def mock_llm_client(self):
"""Create a mock LLM client."""
return Mock(spec=LLMClient)
@pytest.fixture
def engine(self, mock_llm_client):
"""Create escalation engine."""
return EscalationEngine(llm_client=mock_llm_client)
def test_escalate_on_llm_error(self, engine, mock_llm_client):
"""Should escalate gracefully when LLM call fails."""
chunks = [
RetrievedChunk(content="C", score=0.8, source="test.md", chunk_id=0, position=0)
]
mock_llm_client.generate_answer.side_effect = Exception("API error")
decision = engine.process_question("test", chunks)
assert decision.action == "escalation"
assert decision.sources is None
def test_escalate_on_timeout(self, engine, mock_llm_client):
"""Should escalate with timeout message when LLM times out."""
chunks = [
RetrievedChunk(content="C", score=0.8, source="test.md", chunk_id=0, position=0)
]
mock_llm_client.generate_answer.side_effect = RuntimeError("Request timeout")
decision = engine.process_question("test", chunks)
assert decision.action == "escalation"
assert "timeout" in decision.confidence_explanation.lower()
def test_escalate_on_rate_limit(self, engine, mock_llm_client):
"""Should escalate with rate limit message."""
chunks = [
RetrievedChunk(content="C", score=0.8, source="test.md", chunk_id=0, position=0)
]
mock_llm_client.generate_answer.side_effect = RuntimeError("Rate limit exceeded")
decision = engine.process_question("test", chunks)
assert decision.action == "escalation"
assert "rate limit" in decision.confidence_explanation.lower() or "busy" in decision.confidence_explanation.lower()
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