7becffcc89
- Replace useState+useEffect sync pattern with useMemo in EdgeSettingsPanel.tsx to eliminate cascading re-renders - Remove redundant draft state in NodeSettingsPanel.tsx and use store data directly, eliminating useEffect sync loop - Fix mock paths in test_tools.py: patch tavily.TavilyClient and pypdf.PdfReader at their source modules (lazy imports) - Remove unused variable assignment in routes.py (god mode reject) - Remove unused node_lookup dicts in dynamic_graph_builder.py - Remove unused imports across test files (Blueprint, CouncilState, pytest, llm assignments) - Remove unused CouncilBlueprint type import in types.test.ts - Run npm audit fix to resolve moderate vulnerability All 107 backend tests and 26 frontend tests pass. Ruff, ESLint, and TypeScript checks are clean. https://claude.ai/code/session_01XqzyT6fhS8sUe9P5fCmuVU
210 lines
7.7 KiB
Python
210 lines
7.7 KiB
Python
"""
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Tests for the LangGraph routing logic.
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All LLM calls are mocked — no real API calls are made in these tests.
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"""
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import sys
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import os
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
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from unittest.mock import patch, MagicMock
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from state import CouncilState, APPROVAL_THRESHOLD, MAX_ITERATIONS
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from services.graph_builder import route_after_critic, create_initial_state
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class TestRouteAfterCritic:
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"""Unit tests for the conditional edge routing function."""
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def _make_state(self, route_decision: str, iteration_count: int = 1) -> CouncilState:
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state = create_initial_state("test topic", "test-run")
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state["route_decision"] = route_decision
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state["iteration_count"] = iteration_count
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return state
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def test_approve_routes_to_writer(self):
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state = self._make_state("approve")
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assert route_after_critic(state) == "writer_agent"
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def test_rework_routes_to_master(self):
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state = self._make_state("rework")
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assert route_after_critic(state) == "master_agent"
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def test_empty_decision_defaults_to_rework(self):
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state = self._make_state("")
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assert route_after_critic(state) == "master_agent"
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def test_unknown_decision_defaults_to_rework(self):
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state = self._make_state("unknown_value")
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assert route_after_critic(state) == "master_agent"
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class TestCriticAgentParsing:
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"""Unit tests for the critic agent's response parser."""
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def test_parse_valid_approve_response(self):
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from agents.critic_agent import _parse_critic_response
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content = "SCORE: 9\nVERDICT: approve\nFEEDBACK:\nExcellent work."
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score, verdict, feedback = _parse_critic_response(content)
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assert score == 9.0
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assert verdict == "approve"
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assert "Excellent" in feedback
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def test_parse_valid_rework_response(self):
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from agents.critic_agent import _parse_critic_response
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content = "SCORE: 5\nVERDICT: rework\nFEEDBACK:\nNeeds more detail."
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score, verdict, feedback = _parse_critic_response(content)
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assert score == 5.0
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assert verdict == "rework"
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assert "detail" in feedback
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def test_parse_score_clamped_to_0_10(self):
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from agents.critic_agent import _parse_critic_response
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content = "SCORE: 15\nVERDICT: approve\nFEEDBACK:\nToo high score."
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score, verdict, feedback = _parse_critic_response(content)
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assert score == 10.0
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def test_parse_missing_score_defaults_to_0(self):
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from agents.critic_agent import _parse_critic_response
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content = "No structured response at all."
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score, verdict, feedback = _parse_critic_response(content)
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assert score == 0.0
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assert verdict == "rework"
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def test_threshold_boundary_exactly_8_approves(self):
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from agents.critic_agent import _parse_critic_response
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content = f"SCORE: {APPROVAL_THRESHOLD}\nVERDICT: approve\nFEEDBACK:\nGood."
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score, verdict, _ = _parse_critic_response(content)
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assert score == APPROVAL_THRESHOLD
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assert verdict == "approve"
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class TestMasterAgentPromptBuilding:
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"""Unit tests for the master agent's prompt construction."""
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def test_first_iteration_prompt_has_no_feedback_block(self):
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from agents.master_agent import _build_master_prompt
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state = create_initial_state("Test topic", "run-1")
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prompt = _build_master_prompt(state)
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assert "Test topic" in prompt
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assert "feedback" not in prompt.lower() or "Feedback" not in prompt
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def test_rework_prompt_includes_feedback(self):
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from agents.master_agent import _build_master_prompt
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state = create_initial_state("Test topic", "run-1")
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state["current_draft"] = "My draft"
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state["feedback_history"] = ["Score: 5/10\nNeeds more structure."]
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prompt = _build_master_prompt(state)
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assert "My draft" in prompt
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assert "Needs more structure" in prompt
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def test_rework_prompt_includes_all_feedback_rounds(self):
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from agents.master_agent import _build_master_prompt
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state = create_initial_state("Topic", "run-2")
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state["current_draft"] = "Draft v2"
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state["feedback_history"] = ["First feedback", "Second feedback"]
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prompt = _build_master_prompt(state)
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assert "First feedback" in prompt
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assert "Second feedback" in prompt
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assert "2 round" in prompt
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class TestCriticSafetyValve:
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"""Tests for the MAX_ITERATIONS safety valve in the critic agent."""
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def test_safety_valve_forces_approve_at_max_iterations(self):
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from agents.critic_agent import critic_agent_node
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state = create_initial_state("topic", "run-safety")
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state["iteration_count"] = MAX_ITERATIONS
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state["current_draft"] = "Some draft"
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result = critic_agent_node(state)
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assert result["route_decision"] == "approve"
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assert result["critic_score"] == APPROVAL_THRESHOLD
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def test_safety_valve_not_triggered_below_max(self):
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"""Below MAX_ITERATIONS the real LLM call would happen — mock it."""
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from agents.critic_agent import critic_agent_node
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mock_response = MagicMock()
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mock_response.content = "SCORE: 4\nVERDICT: rework\nFEEDBACK:\nNeeds work."
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with patch("agents.critic_agent.ChatAnthropic") as MockLLM:
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MockLLM.return_value.invoke.return_value = mock_response
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state = create_initial_state("topic", "run-below-max")
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state["iteration_count"] = MAX_ITERATIONS - 1
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state["current_draft"] = "Draft"
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result = critic_agent_node(state)
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assert result["route_decision"] == "rework"
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assert result["critic_score"] == 4.0
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class TestMasterAgentNode:
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"""Integration-style tests for master_agent_node with mocked LLM."""
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def test_master_agent_returns_draft(self):
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from agents.master_agent import master_agent_node
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mock_response = MagicMock()
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mock_response.content = "This is a generated draft about AI."
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with patch("agents.master_agent.ChatAnthropic") as MockLLM:
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MockLLM.return_value.invoke.return_value = mock_response
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state = create_initial_state("AI basics", "run-master-1")
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result = master_agent_node(state)
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assert result["current_draft"] == "This is a generated draft about AI."
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assert result["active_node"] == "master_agent"
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assert result["iteration_count"] == 1
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def test_master_agent_increments_iteration_count(self):
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from agents.master_agent import master_agent_node
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mock_response = MagicMock()
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mock_response.content = "Draft"
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with patch("agents.master_agent.ChatAnthropic") as MockLLM:
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MockLLM.return_value.invoke.return_value = mock_response
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state = create_initial_state("topic", "run-master-2")
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state["iteration_count"] = 3
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result = master_agent_node(state)
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assert result["iteration_count"] == 4
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class TestWriterAgentNode:
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"""Tests for writer_agent_node with mocked LLM."""
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def test_writer_returns_polished_draft(self):
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from agents.writer_agent import writer_agent_node
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mock_response = MagicMock()
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mock_response.content = "Polished and professional document."
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with patch("agents.writer_agent.ChatAnthropic") as MockLLM:
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MockLLM.return_value.invoke.return_value = mock_response
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state = create_initial_state("Machine Learning", "run-writer-1")
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state["current_draft"] = "Raw draft content"
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result = writer_agent_node(state)
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assert result["current_draft"] == "Polished and professional document."
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assert result["active_node"] == "writer_agent"
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assert result["route_decision"] == "done"
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