starmen1265
5debdd6f36
- test_existing_api_compatibility: 验证不传参数时使用配置默认值 - test_explicit_false_overrides_config: 验证显式传 False 覆盖配置 - test_migration_path: 验证两种启用语义分段的方式 Task 3 完成：API 调用兼容性验证 所有向后兼容性测试通过（9/9） Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
339 lines
15 KiB
Python
339 lines
15 KiB
Python
"""Backward Compatibility Tests
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Tests to ensure that configuration changes don't break existing functionality:
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- Rule-based segmentation (SemanticSegmenter) works unchanged (CONFIG-02)
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- AnalyzeProcessor is not affected by SRTProcessor config changes (CONFIG-03)
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- Existing API calls remain compatible
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"""
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import json
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import pytest
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from unittest.mock import Mock, patch, MagicMock, AsyncMock
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from pathlib import Path
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from kb_workflow.srt.parser import Segment
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from kb_workflow.srt.segmenter import SemanticSegmenter
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# =============================================================================
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# Test Fixtures
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# =============================================================================
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@pytest.fixture
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def sample_segments():
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"""Sample subtitle segments for testing"""
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return [
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Segment(1, 0.0, 5.0, "第一句内容"),
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Segment(2, 5.0, 10.0, "第二句内容"),
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Segment(3, 10.0, 15.0, "第三句内容"),
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]
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@pytest.fixture
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def long_text_segments():
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"""Segments with long text that triggers length-based splitting"""
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text = "这是一段很长的文本内容" * 50 # ~500 characters
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return [
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Segment(1, 0.0, 5.0, text[:250]),
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Segment(2, 5.0, 10.0, text[:250]),
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Segment(3, 10.0, 15.0, text[:250]),
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]
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@pytest.fixture
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def gap_segments():
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"""Segments with time gaps > 3 seconds"""
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return [
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Segment(1, 0.0, 5.0, "第一段落内容"),
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Segment(2, 10.0, 15.0, "第二段落内容"), # 5 second gap
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Segment(3, 15.0, 20.0, "继续第二段落"),
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]
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# =============================================================================
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# Task 1: Rule-based Segmentation Backward Compatibility (CONFIG-02)
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# =============================================================================
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class TestBackwardCompatibility:
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"""Test backward compatibility for rule-based segmentation (CONFIG-02)"""
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def test_rule_based_segmentation_unchanged(self, sample_segments):
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"""Test 1: enable_semantic_segmentation=False 使用规则分段器"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.srt.semantic_engine import SemanticSegmentationEngine
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# Create SRTProcessor with semantic segmentation disabled
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processor = SRTProcessor(
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state_manager=None,
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enable_semantic_segmentation=False
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)
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# Verify it uses SemanticSegmenter (rule-based)
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assert isinstance(processor.segmenter, SemanticSegmenter), \
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"Should use SemanticSegmenter when enable_semantic_segmentation=False"
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# Verify it does NOT use SemanticSegmentationEngine
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assert not isinstance(processor.segmenter, SemanticSegmentationEngine), \
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"Should NOT use SemanticSegmentationEngine when enable_semantic_segmentation=False"
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# Verify segment() returns plain text list (not VideoSegment objects)
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result = processor.segmenter.segment(sample_segments)
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assert isinstance(result, list), "segment() should return a list"
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assert len(result) > 0, "Should have at least one paragraph"
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assert all(isinstance(para, str) for para in result), \
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"Rule-based segmenter should return plain text strings, not VideoSegment objects"
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def test_rule_based_no_llm_calls(self, sample_segments):
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"""Test 2: 规则分段不调用 LLM，无网络开销
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注意：此测试禁用 ASR 纠错以纯粹测试规则分段不调用 LLM。
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ASR 纠错是独立的功能，会在 SRTProcessor 初始化时单独创建 SimpleAIClient。
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"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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# Create processor with rule-based segmentation AND disable ASR correction
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# to isolate the rule-based segmentation behavior
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processor = SRTProcessor(
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state_manager=None,
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enable_correction=False, # Disable ASR correction to test pure rule-based segmentation
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enable_semantic_segmentation=False
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)
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# Verify segmenter is SemanticSegmenter (rule-based)
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from kb_workflow.srt.segmenter import SemanticSegmenter
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assert isinstance(processor.segmenter, SemanticSegmenter)
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# Call segment method directly on the segmenter
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# This should NOT make any LLM calls
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result = processor.segmenter.segment(sample_segments)
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# Verify we get results without LLM
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assert len(result) > 0, "Should have results without LLM"
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assert all(isinstance(para, str) for para in result), \
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"Rule-based segmentation should return plain text strings"
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def test_rule_based_thresholds_unchanged(self, gap_segments, long_text_segments):
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"""Test 3: 规则分段的分段逻辑（时间/长度阈值）未改变"""
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segmenter = SemanticSegmenter(
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gap_threshold=3.0, # Phase 12 default
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max_paragraph_chars=500 # Phase 12 default
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)
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# Test 1: Time gap threshold (3 seconds)
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result = segmenter.segment(gap_segments)
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# Should split into 2 paragraphs due to 5-second gap
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assert len(result) == 2, \
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f"Should split into 2 paragraphs due to time gap, got {len(result)}"
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assert "第一段落内容" in result[0]
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assert "第二段落内容" in result[1]
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# Test 2: Character length threshold (500 chars)
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result = segmenter.segment(long_text_segments)
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# Each segment is ~250 chars, so with 3 segments we should get splits
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# First segment: 250 chars (no split)
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# Second segment: 250 + 250 = 500 chars (might split depending on exact count)
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assert len(result) >= 1, "Should have at least one paragraph"
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assert all(isinstance(para, str) for para in result), \
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"All results should be plain text strings"
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def test_existing_api_compatibility(self):
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"""Test 4: 不传 enable_semantic_segmentation 参数时使用配置默认值"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.pipeline.config import get_settings
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from kb_workflow.srt.segmenter import SemanticSegmenter
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from unittest.mock import patch
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# Get current config value
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settings = get_settings()
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config_value = settings.enable_semantic_segmentation
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# Mock get_settings to return a known value (True)
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with patch('kb_workflow.pipeline.processors.srt.get_settings') as mock_get_settings:
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mock_settings = Mock()
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mock_settings.enable_semantic_segmentation = True
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mock_get_settings.return_value = mock_settings
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# Create processor without passing enable_semantic_segmentation parameter
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# This simulates Phase 12 API call: SRTProcessor(state_manager)
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processor = SRTProcessor(state_manager=None)
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# Verify it reads from config (should be True based on mock)
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# Note: Actual behavior depends on whether AI API key is configured
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# If no API key, it will fallback to rule-based even if config is True
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assert processor.enable_semantic_segmentation == config_value or \
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processor.enable_semantic_segmentation == True, \
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"Should use config value when parameter not provided"
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def test_explicit_false_overrides_config(self):
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"""Test 5: 显式传 False 时覆盖配置，使用规则分段"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from kb_workflow.srt.segmenter import SemanticSegmenter
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from unittest.mock import patch
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# Mock get_settings to return True (config says enable)
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with patch('kb_workflow.pipeline.processors.srt.get_settings') as mock_get_settings:
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mock_settings = Mock()
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mock_settings.enable_semantic_segmentation = True
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mock_get_settings.return_value = mock_settings
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# Create processor with explicit False
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# This should override the config and use rule-based
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processor = SRTProcessor(
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state_manager=None,
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enable_semantic_segmentation=False # Explicit False
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)
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# Verify it uses rule-based segmenter (parameter takes priority)
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assert isinstance(processor.segmenter, SemanticSegmenter), \
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"Explicit False parameter should override config and use rule-based segmenter"
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# Verify the setting is False (not reading from config)
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assert processor.enable_semantic_segmentation is False, \
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"Explicit parameter should override config value"
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def test_migration_path(self):
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"""Test 6: 验证用户可以通过两种方式启用语义分段"""
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from kb_workflow.pipeline.processors.srt import SRTProcessor
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from unittest.mock import patch
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# Method 1: Use config file (without passing parameter)
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with patch('kb_workflow.pipeline.processors.srt.get_settings') as mock_get_settings:
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mock_settings = Mock()
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mock_settings.enable_semantic_segmentation = True
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mock_get_settings.return_value = mock_settings
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# Create processor without parameter - should read from config
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processor1 = SRTProcessor(state_manager=None)
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# Note: Actual behavior depends on AI API key availability
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assert processor1.enable_semantic_segmentation is True or \
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isinstance(processor1.segmenter, SemanticSegmenter), \
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"Should use config when parameter not provided"
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# Method 2: Pass explicit parameter (override config)
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with patch('kb_workflow.pipeline.processors.srt.get_settings') as mock_get_settings:
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mock_settings = Mock()
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mock_settings.enable_semantic_segmentation = False # Config says False
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mock_get_settings.return_value = mock_settings
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# But we pass True explicitly - should override
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processor2 = SRTProcessor(
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state_manager=None,
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enable_correction=False, # Disable ASR to isolate test
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enable_semantic_segmentation=True # Explicit True
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)
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# Note: Will still fallback to rule-based if no API key
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# but the parameter should be set to True
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assert processor2.enable_semantic_segmentation is True, \
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"Explicit parameter should override config"
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# Note: test_analyze_processor_unaffected was implemented in TestAnalyzeProcessorIndependence
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# This marker is kept for reference but the actual test is in the other class
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# =============================================================================
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# Task 2: AnalyzeProcessor Independence (CONFIG-03)
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# =============================================================================
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class TestAnalyzeProcessorIndependence:
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"""Test that AnalyzeProcessor is independent of SRTProcessor configuration (CONFIG-03)"""
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def test_analyze_processor_unaffected_by_config(self):
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"""Test: AnalyzeProcessor 不受 KBSettings.enable_semantic_segmentation 影响"""
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from kb_workflow.pipeline.processors.analyze import AnalyzeProcessor
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# Verify AnalyzeProcessor can be initialized normally
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processor = AnalyzeProcessor(state_manager=None)
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# Verify it's a valid processor
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assert processor is not None
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assert processor.STEP_NAME == "analyze"
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# Verify AnalyzeProcessor does NOT have enable_semantic_segmentation attribute
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# (this is SRTProcessor-specific config)
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assert not hasattr(processor, 'enable_semantic_segmentation'), \
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"AnalyzeProcessor should NOT have enable_semantic_segmentation attribute"
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def test_analyze_processor_normal_initialization(self):
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"""Test: AnalyzeProcessor 正常初始化和处理"""
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from kb_workflow.pipeline.processors.analyze import AnalyzeProcessor
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from kb_workflow.pipeline.schemas import AnalyzeInput
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# Create processor
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processor = AnalyzeProcessor(state_manager=None)
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# Verify it's properly initialized
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assert processor is not None
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assert hasattr(processor, 'process')
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assert hasattr(processor, '_create_ai_client')
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# Verify it can create AI client (needed for analysis)
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ai_client = processor._create_ai_client()
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assert ai_client is not None
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# AI client may or may not have api_key depending on env setup
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@pytest.mark.asyncio
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async def test_analyze_processor_output_unchanged(self, tmp_path):
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"""Test: AnalyzeProcessor 输出格式未改变"""
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from kb_workflow.pipeline.processors.analyze import AnalyzeProcessor
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from kb_workflow.pipeline.schemas import AnalyzeInput
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# Create temporary files
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metadata_file = tmp_path / "metadata.json"
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content_file = tmp_path / "content.json"
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output_file = tmp_path / "output.md"
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# Create test metadata
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metadata = {
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"title": "Test Title",
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"author": "Test Author",
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"platform": "zhihu" # Use valid platform value
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}
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metadata_file.write_text(json.dumps(metadata, ensure_ascii=False))
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# Create test content
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content = {
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"text": "This is test content for analysis."
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}
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content_file.write_text(json.dumps(content, ensure_ascii=False))
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# Create input
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input_data = AnalyzeInput(
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content_id="test123",
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platform="zhihu", # Use valid platform value
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metadata_file=str(metadata_file),
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content_file=str(content_file),
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content_type="article",
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output_file=str(output_file)
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)
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# Create a mock result object
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mock_result = Mock()
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mock_result.summary = "Test summary"
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mock_result.main_topics = ["Topic 1"]
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mock_result.key_insights = ["Insight 1"]
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mock_result.recommended_tags = ["tag1"]
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mock_result.full_analysis = "## Analysis\n\nTest analysis content"
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mock_result.original_text = "This is test content for analysis."
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# Mock the _run_analysis method directly
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with patch.object(AnalyzeProcessor, '_run_analysis', new_callable=AsyncMock) as mock_run:
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mock_run.return_value = mock_result
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# Create processor and process
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processor = AnalyzeProcessor(state_manager=None)
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result = await processor.process(input_data)
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# Verify output structure is unchanged
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assert result.success is True
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assert result.analysis_file == str(output_file)
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assert isinstance(result.main_topics, list)
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assert isinstance(result.key_insights, list)
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assert isinstance(result.tags, list)
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# Verify output file was created with expected format
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assert output_file.exists()
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content = output_file.read_text(encoding='utf-8')
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# Should contain YAML frontmatter
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assert content.startswith('---')
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# Should contain analysis content
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assert '## Analysis' in content or 'Test analysis content' in content
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