test: add comprehensive tests for ai.py (13% -> 98% coverage) (#64)
- Test LLM response parsing (list and dict formats) - Test null params conversion to empty dict - Test invalid format handling and skipping - Test emotion update logic with all emotion types - Test fallback to CALM on invalid/missing emotion - Test batch avatar processing - Test AI.decide wrapper returns NULL_EVENT - Test thinking field variants (avatar_thinking vs thinking) - Add testing strategy documentation to test file Closes #63
This commit is contained in:
Zihao Xu
GitHub
608
tests/test_ai.py
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608
tests/test_ai.py
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@@ -0,0 +1,608 @@
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"""
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Tests for src/classes/ai.py
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This module tests the NPC AI decision-making system, including:
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- LLM response parsing
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- Emotion updates
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- Batch avatar processing
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- Edge cases
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Testing Strategy:
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We do NOT call real LLM APIs in tests. Instead, we mock `call_llm_with_task_name`
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to simulate LLM responses. This approach:
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1. Avoids API call costs.
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2. Ensures deterministic test results (real LLM responses are unpredictable).
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3. Allows testing edge cases (invalid formats, empty responses, etc.).
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Example:
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with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
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mock_llm.return_value = {
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"AvatarName": {
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"action_name_params_pairs": [["cultivate", {"duration": 10}]],
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"avatar_thinking": "...",
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"current_emotion": "平静"
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}
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}
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results = await ai._decide(world, [avatar])
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What we're testing:
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- NOT whether the LLM answers correctly.
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- BUT whether ai.py correctly parses LLM responses and handles edge cases.
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"""
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import pytest
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from unittest.mock import AsyncMock, MagicMock, patch
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from src.classes.ai import AI, LLMAI, llm_ai
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from src.classes.emotions import EmotionType
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from src.classes.event import NULL_EVENT
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class TestLLMAIDecide:
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"""Tests for LLMAI._decide method."""
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@pytest.fixture
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def mock_world(self, base_world):
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"""Create a world with mocked methods."""
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base_world.get_info = MagicMock(return_value="world info")
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base_world.get_observable_avatars = MagicMock(return_value=[])
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return base_world
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@pytest.fixture
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def test_avatar(self, dummy_avatar):
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"""Create an avatar with mocked methods."""
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dummy_avatar.get_expanded_info = MagicMock(return_value="avatar info")
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dummy_avatar.emotion = EmotionType.CALM
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return dummy_avatar
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@pytest.mark.asyncio
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async def test_decide_with_valid_list_format(self, mock_world, test_avatar):
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"""Test that LLM response with list format [name, params] is parsed correctly."""
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mock_response = {
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test_avatar.name: {
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"action_name_params_pairs": [
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["cultivate", {"duration": 10}],
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["move", {"target_x": 5, "target_y": 5}]
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],
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"avatar_thinking": "I should cultivate to get stronger.",
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"short_term_objective": "Reach Foundation Establishment",
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"current_emotion": "平静"
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}
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}
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ai = LLMAI()
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with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
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mock_llm.return_value = mock_response
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results = await ai._decide(mock_world, [test_avatar])
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assert test_avatar in results
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pairs, thinking, objective = results[test_avatar]
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assert len(pairs) == 2
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assert pairs[0] == ("cultivate", {"duration": 10})
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assert pairs[1] == ("move", {"target_x": 5, "target_y": 5})
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assert thinking == "I should cultivate to get stronger."
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assert objective == "Reach Foundation Establishment"
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@pytest.mark.asyncio
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async def test_decide_with_valid_dict_format(self, mock_world, test_avatar):
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"""Test that LLM response with dict format {action_name, action_params} is parsed correctly."""
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mock_response = {
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test_avatar.name: {
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"action_name_params_pairs": [
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{"action_name": "cultivate", "action_params": {"duration": 10}},
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{"action_name": "rest", "action_params": {}}
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],
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"avatar_thinking": "Time to rest after cultivation.",
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"short_term_objective": "Recover energy",
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"current_emotion": "疲惫"
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}
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}
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ai = LLMAI()
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with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
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mock_llm.return_value = mock_response
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results = await ai._decide(mock_world, [test_avatar])
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assert test_avatar in results
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pairs, thinking, objective = results[test_avatar]
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assert len(pairs) == 2
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assert pairs[0] == ("cultivate", {"duration": 10})
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assert pairs[1] == ("rest", {})
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assert test_avatar.emotion == EmotionType.TIRED
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@pytest.mark.asyncio
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async def test_decide_with_null_params_converts_to_empty_dict(self, mock_world, test_avatar):
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"""Test that null/None params are converted to empty dict."""
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mock_response = {
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test_avatar.name: {
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"action_name_params_pairs": [
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["cultivate", None], # List format with null
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{"action_name": "rest", "action_params": None} # Dict format with null
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],
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"avatar_thinking": "Just cultivating.",
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"short_term_objective": "Get stronger",
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"current_emotion": "平静"
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}
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}
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ai = LLMAI()
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with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
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mock_llm.return_value = mock_response
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results = await ai._decide(mock_world, [test_avatar])
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assert test_avatar in results
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pairs, _, _ = results[test_avatar]
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# Both should have empty dict as params.
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assert pairs[0] == ("cultivate", {})
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assert pairs[1] == ("rest", {})
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@pytest.mark.asyncio
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async def test_decide_with_invalid_format_skips_pair(self, mock_world, test_avatar):
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"""Test that invalid pair formats are skipped, valid ones kept."""
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mock_response = {
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test_avatar.name: {
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"action_name_params_pairs": [
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["cultivate", {"duration": 10}], # Valid list format
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"invalid_string", # Invalid: not list/dict
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["only_one_element"], # Invalid: list with 1 element
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{"action_name": "move"}, # Invalid: missing action_params
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{"wrong_key": "rest", "action_params": {}}, # Invalid: wrong key
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{"action_name": "rest", "action_params": {}}, # Valid dict format
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],
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"avatar_thinking": "Mixed formats.",
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"short_term_objective": "Test edge cases",
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"current_emotion": "平静"
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}
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}
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ai = LLMAI()
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with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
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mock_llm.return_value = mock_response
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results = await ai._decide(mock_world, [test_avatar])
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assert test_avatar in results
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pairs, _, _ = results[test_avatar]
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# Only 2 valid pairs should be kept.
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assert len(pairs) == 2
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assert pairs[0] == ("cultivate", {"duration": 10})
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assert pairs[1] == ("rest", {})
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@pytest.mark.asyncio
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async def test_decide_with_empty_response_skips_avatar(self, mock_world, test_avatar):
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"""Test that empty LLM response skips the avatar."""
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ai = LLMAI()
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with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
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mock_llm.return_value = {} # Empty response
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results = await ai._decide(mock_world, [test_avatar])
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assert test_avatar not in results
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assert results == {}
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@pytest.mark.asyncio
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async def test_decide_with_none_response_skips_avatar(self, mock_world, test_avatar):
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"""Test that None LLM response skips the avatar."""
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ai = LLMAI()
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with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
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mock_llm.return_value = None # None response
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results = await ai._decide(mock_world, [test_avatar])
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assert test_avatar not in results
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@pytest.mark.asyncio
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async def test_decide_with_missing_avatar_name_skips(self, mock_world, test_avatar):
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"""Test that response without avatar name skips that avatar."""
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mock_response = {
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"OtherAvatar": { # Different name than test_avatar.name
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"action_name_params_pairs": [["cultivate", {}]],
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"avatar_thinking": "Should not be used.",
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"short_term_objective": "Not for test avatar",
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"current_emotion": "平静"
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}
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}
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ai = LLMAI()
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with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
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mock_llm.return_value = mock_response
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results = await ai._decide(mock_world, [test_avatar])
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assert test_avatar not in results
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@pytest.mark.asyncio
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async def test_decide_with_no_valid_pairs_skips_avatar(self, mock_world, test_avatar):
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"""Test that avatar is skipped when all pairs are invalid."""
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mock_response = {
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test_avatar.name: {
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"action_name_params_pairs": [
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"invalid",
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123,
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None,
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],
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"avatar_thinking": "All invalid.",
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"short_term_objective": "Test",
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"current_emotion": "平静"
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}
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}
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ai = LLMAI()
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with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
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mock_llm.return_value = mock_response
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results = await ai._decide(mock_world, [test_avatar])
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# Avatar should be skipped since no valid pairs.
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assert test_avatar not in results
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class TestLLMAIEmotionUpdate:
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"""Tests for emotion update logic in LLMAI._decide."""
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@pytest.fixture
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def mock_world(self, base_world):
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"""Create a world with mocked methods."""
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base_world.get_info = MagicMock(return_value="world info")
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base_world.get_observable_avatars = MagicMock(return_value=[])
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return base_world
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@pytest.fixture
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def test_avatar(self, dummy_avatar):
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"""Create an avatar with mocked methods."""
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dummy_avatar.get_expanded_info = MagicMock(return_value="avatar info")
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dummy_avatar.emotion = EmotionType.CALM
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return dummy_avatar
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@pytest.mark.asyncio
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async def test_decide_updates_emotion_with_valid_value(self, mock_world, test_avatar):
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"""Test that valid emotion string updates avatar.emotion correctly."""
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emotions_to_test = [
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("开心", EmotionType.HAPPY),
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("愤怒", EmotionType.ANGRY),
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("悲伤", EmotionType.SAD),
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("恐惧", EmotionType.FEARFUL),
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("惊讶", EmotionType.SURPRISED),
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("期待", EmotionType.ANTICIPATING),
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("厌恶", EmotionType.DISGUSTED),
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("疑惑", EmotionType.CONFUSED),
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("疲惫", EmotionType.TIRED),
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]
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ai = LLMAI()
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for emotion_str, expected_emotion in emotions_to_test:
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test_avatar.emotion = EmotionType.CALM # Reset
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mock_response = {
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test_avatar.name: {
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"action_name_params_pairs": [["cultivate", {}]],
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"avatar_thinking": "Testing emotion.",
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"short_term_objective": "Test",
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"current_emotion": emotion_str
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}
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}
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with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
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mock_llm.return_value = mock_response
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await ai._decide(mock_world, [test_avatar])
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assert test_avatar.emotion == expected_emotion, \
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f"Expected {expected_emotion} for '{emotion_str}', got {test_avatar.emotion}"
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@pytest.mark.asyncio
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async def test_decide_fallback_to_calm_on_invalid_emotion(self, mock_world, test_avatar):
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"""Test that invalid emotion falls back to CALM."""
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test_avatar.emotion = EmotionType.HAPPY # Set to non-CALM first
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mock_response = {
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test_avatar.name: {
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"action_name_params_pairs": [["cultivate", {}]],
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"avatar_thinking": "Testing emotion.",
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"short_term_objective": "Test",
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"current_emotion": "InvalidEmotion" # Invalid value
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}
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}
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ai = LLMAI()
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with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
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mock_llm.return_value = mock_response
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await ai._decide(mock_world, [test_avatar])
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assert test_avatar.emotion == EmotionType.CALM
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@pytest.mark.asyncio
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async def test_decide_fallback_to_calm_on_missing_emotion(self, mock_world, test_avatar):
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"""Test that missing emotion field falls back to CALM (default '平静')."""
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test_avatar.emotion = EmotionType.HAPPY # Set to non-CALM first
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mock_response = {
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test_avatar.name: {
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"action_name_params_pairs": [["cultivate", {}]],
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"avatar_thinking": "Testing emotion.",
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"short_term_objective": "Test"
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# No current_emotion field
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}
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}
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ai = LLMAI()
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with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
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mock_llm.return_value = mock_response
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await ai._decide(mock_world, [test_avatar])
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# Default is "平静" which maps to CALM.
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assert test_avatar.emotion == EmotionType.CALM
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class TestLLMAIBatchProcessing:
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"""Tests for batch avatar processing in LLMAI._decide."""
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@pytest.fixture
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def mock_world(self, base_world):
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"""Create a world with mocked methods."""
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base_world.get_info = MagicMock(return_value="world info")
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base_world.get_observable_avatars = MagicMock(return_value=[])
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return base_world
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@pytest.fixture
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def avatar_a(self, mock_world):
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"""Create first test avatar."""
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from src.classes.avatar import Avatar, Gender
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from src.classes.age import Age
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from src.classes.cultivation import Realm
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from src.classes.calendar import Year, Month, create_month_stamp
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from src.classes.root import Root
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from src.classes.alignment import Alignment
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from src.utils.id_generator import get_avatar_id
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av = Avatar(
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world=mock_world,
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name="AvatarA",
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id=get_avatar_id(),
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birth_month_stamp=create_month_stamp(Year(2000), Month.JANUARY),
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age=Age(20, Realm.Qi_Refinement),
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gender=Gender.MALE,
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pos_x=0,
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pos_y=0,
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root=Root.GOLD,
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personas=[],
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alignment=Alignment.RIGHTEOUS
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)
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av.get_expanded_info = MagicMock(return_value="avatar a info")
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av.weapon = MagicMock()
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av.weapon.get_detailed_info.return_value = "Test Weapon"
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av.emotion = EmotionType.CALM
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return av
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|
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@pytest.fixture
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def avatar_b(self, mock_world):
|
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"""Create second test avatar."""
|
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from src.classes.avatar import Avatar, Gender
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from src.classes.age import Age
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from src.classes.cultivation import Realm
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from src.classes.calendar import Year, Month, create_month_stamp
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from src.classes.root import Root
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from src.classes.alignment import Alignment
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from src.utils.id_generator import get_avatar_id
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av = Avatar(
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world=mock_world,
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name="AvatarB",
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id=get_avatar_id(),
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birth_month_stamp=create_month_stamp(Year(2000), Month.JANUARY),
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age=Age(25, Realm.Foundation_Establishment),
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gender=Gender.FEMALE,
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pos_x=1,
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pos_y=1,
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root=Root.WATER,
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personas=[],
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alignment=Alignment.NEUTRAL
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)
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av.get_expanded_info = MagicMock(return_value="avatar b info")
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av.weapon = MagicMock()
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av.weapon.get_detailed_info.return_value = "Test Weapon B"
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av.emotion = EmotionType.CALM
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return av
|
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|
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@pytest.mark.asyncio
|
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async def test_decide_multiple_avatars_concurrently(self, mock_world, avatar_a, avatar_b):
|
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"""Test that multiple avatars are processed and each gets correct results."""
|
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call_count = 0
|
||||
|
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async def mock_llm_side_effect(task_name, template_path, info):
|
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nonlocal call_count
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call_count += 1
|
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avatar_name = info["avatar_name"]
|
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|
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if avatar_name == "AvatarA":
|
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return {
|
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"AvatarA": {
|
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"action_name_params_pairs": [["cultivate", {"duration": 10}]],
|
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"avatar_thinking": "A is cultivating.",
|
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"short_term_objective": "A's goal",
|
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"current_emotion": "开心"
|
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}
|
||||
}
|
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else:
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return {
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"AvatarB": {
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"action_name_params_pairs": [["move", {"target_x": 2, "target_y": 2}]],
|
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"avatar_thinking": "B is moving.",
|
||||
"short_term_objective": "B's goal",
|
||||
"current_emotion": "愤怒"
|
||||
}
|
||||
}
|
||||
|
||||
ai = LLMAI()
|
||||
with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
|
||||
mock_llm.side_effect = mock_llm_side_effect
|
||||
results = await ai._decide(mock_world, [avatar_a, avatar_b])
|
||||
|
||||
# Both avatars should have results.
|
||||
assert avatar_a in results
|
||||
assert avatar_b in results
|
||||
|
||||
# LLM should be called twice (once per avatar).
|
||||
assert call_count == 2
|
||||
|
||||
# Check avatar A's results.
|
||||
pairs_a, thinking_a, objective_a = results[avatar_a]
|
||||
assert pairs_a == [("cultivate", {"duration": 10})]
|
||||
assert thinking_a == "A is cultivating."
|
||||
assert avatar_a.emotion == EmotionType.HAPPY
|
||||
|
||||
# Check avatar B's results.
|
||||
pairs_b, thinking_b, objective_b = results[avatar_b]
|
||||
assert pairs_b == [("move", {"target_x": 2, "target_y": 2})]
|
||||
assert thinking_b == "B is moving."
|
||||
assert avatar_b.emotion == EmotionType.ANGRY
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_decide_with_empty_avatar_list(self, mock_world):
|
||||
"""Test that empty avatar list returns empty results."""
|
||||
ai = LLMAI()
|
||||
with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
|
||||
results = await ai._decide(mock_world, [])
|
||||
|
||||
assert results == {}
|
||||
mock_llm.assert_not_called()
|
||||
|
||||
|
||||
class TestAIDecideWrapper:
|
||||
"""Tests for AI.decide wrapper method."""
|
||||
|
||||
@pytest.fixture
|
||||
def mock_world(self, base_world):
|
||||
"""Create a world with mocked methods."""
|
||||
base_world.get_info = MagicMock(return_value="world info")
|
||||
base_world.get_observable_avatars = MagicMock(return_value=[])
|
||||
return base_world
|
||||
|
||||
@pytest.fixture
|
||||
def test_avatar(self, dummy_avatar):
|
||||
"""Create an avatar with mocked methods."""
|
||||
dummy_avatar.get_expanded_info = MagicMock(return_value="avatar info")
|
||||
dummy_avatar.emotion = EmotionType.CALM
|
||||
return dummy_avatar
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_decide_returns_null_event(self, mock_world, test_avatar):
|
||||
"""Test that AI.decide returns NULL_EVENT for each avatar."""
|
||||
mock_response = {
|
||||
test_avatar.name: {
|
||||
"action_name_params_pairs": [["cultivate", {}]],
|
||||
"avatar_thinking": "Testing.",
|
||||
"short_term_objective": "Test",
|
||||
"current_emotion": "平静"
|
||||
}
|
||||
}
|
||||
|
||||
ai = LLMAI()
|
||||
with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
|
||||
mock_llm.return_value = mock_response
|
||||
# Call decide (not _decide) to test the wrapper.
|
||||
results = await ai.decide(mock_world, [test_avatar])
|
||||
|
||||
assert test_avatar in results
|
||||
pairs, thinking, objective, event = results[test_avatar]
|
||||
|
||||
# Event should be NULL_EVENT.
|
||||
assert event is NULL_EVENT
|
||||
|
||||
# Other fields should be preserved.
|
||||
assert pairs == [("cultivate", {})]
|
||||
assert thinking == "Testing."
|
||||
assert objective == "Test"
|
||||
|
||||
|
||||
class TestLLMAIThinkingFieldVariants:
|
||||
"""Tests for different thinking field names in LLM response."""
|
||||
|
||||
@pytest.fixture
|
||||
def mock_world(self, base_world):
|
||||
"""Create a world with mocked methods."""
|
||||
base_world.get_info = MagicMock(return_value="world info")
|
||||
base_world.get_observable_avatars = MagicMock(return_value=[])
|
||||
return base_world
|
||||
|
||||
@pytest.fixture
|
||||
def test_avatar(self, dummy_avatar):
|
||||
"""Create an avatar with mocked methods."""
|
||||
dummy_avatar.get_expanded_info = MagicMock(return_value="avatar info")
|
||||
dummy_avatar.emotion = EmotionType.CALM
|
||||
return dummy_avatar
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_decide_with_thinking_field(self, mock_world, test_avatar):
|
||||
"""Test that 'thinking' field is used as fallback for avatar_thinking."""
|
||||
mock_response = {
|
||||
test_avatar.name: {
|
||||
"action_name_params_pairs": [["cultivate", {}]],
|
||||
"thinking": "Using thinking field.", # Not avatar_thinking
|
||||
"short_term_objective": "Test",
|
||||
"current_emotion": "平静"
|
||||
}
|
||||
}
|
||||
|
||||
ai = LLMAI()
|
||||
with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
|
||||
mock_llm.return_value = mock_response
|
||||
results = await ai._decide(mock_world, [test_avatar])
|
||||
|
||||
assert test_avatar in results
|
||||
_, thinking, _ = results[test_avatar]
|
||||
assert thinking == "Using thinking field."
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_decide_prefers_avatar_thinking_over_thinking(self, mock_world, test_avatar):
|
||||
"""Test that 'avatar_thinking' takes precedence over 'thinking'."""
|
||||
mock_response = {
|
||||
test_avatar.name: {
|
||||
"action_name_params_pairs": [["cultivate", {}]],
|
||||
"avatar_thinking": "Preferred field.",
|
||||
"thinking": "Fallback field.",
|
||||
"short_term_objective": "Test",
|
||||
"current_emotion": "平静"
|
||||
}
|
||||
}
|
||||
|
||||
ai = LLMAI()
|
||||
with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
|
||||
mock_llm.return_value = mock_response
|
||||
results = await ai._decide(mock_world, [test_avatar])
|
||||
|
||||
assert test_avatar in results
|
||||
_, thinking, _ = results[test_avatar]
|
||||
assert thinking == "Preferred field."
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_decide_with_missing_optional_fields(self, mock_world, test_avatar):
|
||||
"""Test that missing optional fields default to empty strings."""
|
||||
mock_response = {
|
||||
test_avatar.name: {
|
||||
"action_name_params_pairs": [["cultivate", {}]],
|
||||
# No avatar_thinking, thinking, or short_term_objective
|
||||
"current_emotion": "平静"
|
||||
}
|
||||
}
|
||||
|
||||
ai = LLMAI()
|
||||
with patch("src.classes.ai.call_llm_with_task_name", new_callable=AsyncMock) as mock_llm:
|
||||
mock_llm.return_value = mock_response
|
||||
results = await ai._decide(mock_world, [test_avatar])
|
||||
|
||||
assert test_avatar in results
|
||||
_, thinking, objective = results[test_avatar]
|
||||
assert thinking == ""
|
||||
assert objective == ""
|
||||
|
||||
|
||||
class TestLLMAIModuleLevelInstance:
|
||||
"""Tests for the module-level llm_ai instance."""
|
||||
|
||||
def test_llm_ai_is_llmai_instance(self):
|
||||
"""Test that llm_ai is an instance of LLMAI."""
|
||||
assert isinstance(llm_ai, LLMAI)
|
||||
|
||||
def test_llm_ai_is_ai_subclass(self):
|
||||
"""Test that LLMAI is a subclass of AI."""
|
||||
assert issubclass(LLMAI, AI)
|
||||
Reference in New Issue
Block a user