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"""Unit tests for Phase 7: Judge integration in iterative research flow."""
from unittest.mock import AsyncMock, MagicMock, patch
import pytest
from src.orchestrator.research_flow import IterativeResearchFlow
from src.utils.models import (
AgentSelectionPlan,
AgentTask,
AssessmentDetails,
JudgeAssessment,
KnowledgeGapOutput,
ToolAgentOutput,
)
@pytest.fixture
def mock_judge_handler():
"""Create a mock judge handler."""
judge = MagicMock()
judge.assess = AsyncMock()
return judge
def create_judge_assessment(
sufficient: bool,
confidence: float,
recommendation: str,
reasoning: str,
) -> JudgeAssessment:
"""Helper to create a valid JudgeAssessment."""
return JudgeAssessment(
details=AssessmentDetails(
mechanism_score=5,
mechanism_reasoning="Test mechanism reasoning that is long enough",
clinical_evidence_score=5,
clinical_reasoning="Test clinical reasoning that is long enough",
drug_candidates=[],
key_findings=[],
),
sufficient=sufficient,
confidence=confidence,
recommendation=recommendation,
reasoning=reasoning,
)
@pytest.fixture
def mock_agents():
"""Create mock agents for the flow."""
return {
"knowledge_gap": AsyncMock(),
"tool_selector": AsyncMock(),
"thinking": AsyncMock(),
"writer": AsyncMock(),
}
@pytest.fixture
def flow_with_judge(mock_agents, mock_judge_handler):
"""Create an IterativeResearchFlow with mocked agents and judge."""
with (
patch("src.orchestrator.research_flow.create_knowledge_gap_agent") as mock_kg,
patch("src.orchestrator.research_flow.create_tool_selector_agent") as mock_ts,
patch("src.orchestrator.research_flow.create_thinking_agent") as mock_thinking,
patch("src.orchestrator.research_flow.create_writer_agent") as mock_writer,
patch("src.orchestrator.research_flow.create_judge_handler") as mock_judge_factory,
patch("src.orchestrator.research_flow.execute_tool_tasks") as mock_execute,
patch("src.orchestrator.research_flow.get_workflow_state") as mock_state,
):
mock_kg.return_value = mock_agents["knowledge_gap"]
mock_ts.return_value = mock_agents["tool_selector"]
mock_thinking.return_value = mock_agents["thinking"]
mock_writer.return_value = mock_agents["writer"]
mock_judge_factory.return_value = mock_judge_handler
mock_execute.return_value = {
"task_1": ToolAgentOutput(output="Finding 1", sources=["url1"]),
}
# Mock workflow state
mock_state_obj = MagicMock()
mock_state_obj.evidence = []
mock_state_obj.add_evidence = MagicMock(return_value=1)
mock_state.return_value = mock_state_obj
return IterativeResearchFlow(max_iterations=2, max_time_minutes=5)
@pytest.mark.unit
@pytest.mark.asyncio
class TestJudgeIntegration:
"""Tests for judge integration in iterative research flow."""
async def test_judge_called_after_tool_execution(
self, flow_with_judge, mock_agents, mock_judge_handler
):
"""Judge should be called after tool execution."""
# Mock knowledge gap agent to return incomplete
mock_agents["knowledge_gap"].evaluate = AsyncMock(
return_value=KnowledgeGapOutput(
research_complete=False,
outstanding_gaps=["Need more info"],
)
)
# Mock thinking agent
mock_agents["thinking"].generate_observations = AsyncMock(return_value="Initial thoughts")
# Mock tool selector
mock_agents["tool_selector"].select_tools = AsyncMock(
return_value=AgentSelectionPlan(
tasks=[
AgentTask(
gap="Need more info",
agent="WebSearchAgent",
query="test query",
)
]
)
)
# Mock judge to return sufficient
mock_judge_handler.assess = AsyncMock(
return_value=create_judge_assessment(
sufficient=True,
confidence=0.9,
recommendation="synthesize",
reasoning="Evidence is sufficient to provide a comprehensive answer.",
)
)
# Mock writer
mock_agents["writer"].write_report = AsyncMock(
return_value="# Final Report\n\nContent here."
)
result = await flow_with_judge.run("Test query")
# Verify judge was called
assert mock_judge_handler.assess.called
assert isinstance(result, str)
assert "Final Report" in result
async def test_loop_completes_when_judge_says_sufficient(
self, flow_with_judge, mock_agents, mock_judge_handler
):
"""Loop should complete when judge says evidence is sufficient."""
# Mock knowledge gap to return incomplete
mock_agents["knowledge_gap"].evaluate = AsyncMock(
return_value=KnowledgeGapOutput(
research_complete=False,
outstanding_gaps=["Need more info"],
)
)
mock_agents["thinking"].generate_observations = AsyncMock(return_value="Thoughts")
mock_agents["tool_selector"].select_tools = AsyncMock(
return_value=AgentSelectionPlan(
tasks=[
AgentTask(
gap="Need more info",
agent="WebSearchAgent",
query="test",
)
]
)
)
# Judge says sufficient
mock_judge_handler.assess = AsyncMock(
return_value=create_judge_assessment(
sufficient=True,
confidence=0.95,
recommendation="synthesize",
reasoning="Enough evidence has been collected to synthesize a comprehensive answer.",
)
)
mock_agents["writer"].write_report = AsyncMock(return_value="# Report\n\nDone.")
result = await flow_with_judge.run("Test query")
# Should complete after judge says sufficient
assert flow_with_judge.should_continue is False
assert mock_judge_handler.assess.called
assert isinstance(result, str)
async def test_loop_continues_when_judge_says_insufficient(
self, flow_with_judge, mock_agents, mock_judge_handler
):
"""Loop should continue when judge says evidence is insufficient."""
call_count = {"kg": 0, "judge": 0}
def mock_kg_evaluate(*args, **kwargs):
call_count["kg"] += 1
if call_count["kg"] == 1:
return KnowledgeGapOutput(
research_complete=False,
outstanding_gaps=["Need more info"],
)
# Second call: complete
return KnowledgeGapOutput(
research_complete=True,
outstanding_gaps=[],
)
def mock_judge_assess(*args, **kwargs):
call_count["judge"] += 1
# First call: insufficient
if call_count["judge"] == 1:
return create_judge_assessment(
sufficient=False,
confidence=0.5,
recommendation="continue",
reasoning="Need more evidence to provide a comprehensive answer.",
)
# Second call: sufficient (but won't be reached due to max_iterations)
return create_judge_assessment(
sufficient=True,
confidence=0.9,
recommendation="synthesize",
reasoning="Enough evidence has now been collected to proceed.",
)
mock_agents["knowledge_gap"].evaluate = AsyncMock(side_effect=mock_kg_evaluate)
mock_agents["thinking"].generate_observations = AsyncMock(return_value="Thoughts")
mock_agents["tool_selector"].select_tools = AsyncMock(
return_value=AgentSelectionPlan(
tasks=[
AgentTask(
gap="Need more info",
agent="WebSearchAgent",
query="test",
)
]
)
)
mock_judge_handler.assess = AsyncMock(side_effect=mock_judge_assess)
mock_agents["writer"].write_report = AsyncMock(return_value="# Report\n\nDone.")
result = await flow_with_judge.run("Test query")
# Judge should be called
assert mock_judge_handler.assess.called
# Should eventually complete
assert isinstance(result, str)
async def test_judge_receives_evidence_from_state(
self, flow_with_judge, mock_agents, mock_judge_handler
):
"""Judge should receive evidence from workflow state."""
from src.utils.models import Citation, Evidence
# Create mock evidence in state
mock_evidence = [
Evidence(
content="Test evidence content",
citation=Citation(
source="rag", # Use valid SourceName
title="Test Title",
url="https://example.com",
date="2024-01-01",
authors=[],
),
relevance=0.8,
)
]
# Mock state to return evidence
from unittest.mock import patch
with patch("src.orchestrator.research_flow.get_workflow_state") as mock_state:
mock_state_obj = MagicMock()
mock_state_obj.evidence = mock_evidence
mock_state_obj.add_evidence = MagicMock(return_value=1)
mock_state.return_value = mock_state_obj
mock_agents["knowledge_gap"].evaluate = AsyncMock(
return_value=KnowledgeGapOutput(
research_complete=False,
outstanding_gaps=["Need info"],
)
)
mock_agents["thinking"].generate_observations = AsyncMock(return_value="Thoughts")
mock_agents["tool_selector"].select_tools = AsyncMock(
return_value=AgentSelectionPlan(
tasks=[
AgentTask(
gap="Need info",
agent="WebSearchAgent",
query="test",
)
]
)
)
mock_judge_handler.assess = AsyncMock(
return_value=create_judge_assessment(
sufficient=True,
confidence=0.9,
recommendation="synthesize",
reasoning="Good evidence has been collected to answer the query.",
)
)
mock_agents["writer"].write_report = AsyncMock(return_value="# Report\n\nDone.")
result = await flow_with_judge.run("Test query")
# Verify judge was called with evidence
assert mock_judge_handler.assess.called
call_args = mock_judge_handler.assess.call_args
assert call_args[0][0] == "Test query" # query
assert len(call_args[0][1]) >= 0 # evidence list
assert isinstance(result, str)
async def test_token_tracking_for_judge_call(
self, flow_with_judge, mock_agents, mock_judge_handler
):
"""Token tracking should work for judge calls."""
mock_agents["knowledge_gap"].evaluate = AsyncMock(
return_value=KnowledgeGapOutput(
research_complete=False,
outstanding_gaps=["Need info"],
)
)
mock_agents["thinking"].generate_observations = AsyncMock(return_value="Thoughts")
mock_agents["tool_selector"].select_tools = AsyncMock(
return_value=AgentSelectionPlan(
tasks=[
AgentTask(
gap="Need info",
agent="WebSearchAgent",
query="test",
)
]
)
)
mock_judge_handler.assess = AsyncMock(
return_value=create_judge_assessment(
sufficient=True,
confidence=0.9,
recommendation="synthesize",
reasoning="Evidence is sufficient to provide a comprehensive answer.",
)
)
mock_agents["writer"].write_report = AsyncMock(return_value="# Report\n\nDone.")
await flow_with_judge.run("Test query")
# Check that tokens were tracked for the iteration
iteration_tokens = flow_with_judge.budget_tracker.get_iteration_tokens(
flow_with_judge.loop_id, 1
)
# Should have tracked tokens (may be 0 if estimation is off, but method should work)
assert isinstance(iteration_tokens, int)
assert iteration_tokens >= 0
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