test(14-00): add test infrastructure for robustness and cost control

- Add comprehensive test suite in test_semantic_robustness.py
  - TestLLMFailureFallback: LLM调用失败自动降级 (ROBUST-01)
  - TestNonRecoverableErrorDetection: 不可恢复错误检测
  - TestRecoverableErrorDetection: 可恢复错误检测
  - TestRetryMechanism: 重试机制测试
  - TestFallbackToRuleBased: 降级到规则分段 (ROBUST-03)
  - TestLoggingBehavior: 日志记录行为 (ROBUST-04)
  - TestParseFailureRetry: 解析失败重试 (ROBUST-02)
  - TestExtractJSON: JSON提取功能

- Update conftest.py with Phase 14 fixtures:
  - Mock AI clients for different failure scenarios
  - Long text samples for chunking tests (COST-01)
  - Cache-related fixtures (COST-02)
  - Long subtitle segments for testing

- Add TestSemanticSegmentationToggle to test_srt_processor.py (COST-03):
  - Test enabling/disabling semantic segmentation
  - Test cache enable/disable functionality
  - Test configuration switches

All tests follow TDD RED state - failing tests for future implementation

tests/conftest.py

@@ -2,8 +2,11 @@
 
 from typing import Optional
 from unittest.mock import Mock
+from pathlib import Path
+from requests.exceptions import ConnectionError, Timeout, HTTPError
 
 import pytest
+from kb_workflow.srt.parser import Segment
 
 
 class MockAIClient:
@@ -60,3 +63,122 @@ def sample_metadata_movie():
         "tags": ["科幻", "电影"],
         "description": "一部关于太空探索的科幻电影"
     }
+
+
+# ============================================================================
+# Phase 14 Fixtures: 鲁棒性和成本控制测试
+# ============================================================================
+
+@pytest.fixture
+def long_text_sample():
+    """长文本样本（>5000 字符，用于分块测试）"""
+    # 生成一个超过 5000 字符的文本，包含多个句子
+    sentences = ["这是一个测试句子。" * 10] * 15  # 约 6000 字符
+    return " ".join(sentences)
+
+
+@pytest.fixture
+def mock_ai_client_network_error():
+    """模拟网络错误的 AI 客户端"""
+    client = Mock()
+    client.api_key = "test_key"
+    client.generate = Mock(side_effect=ConnectionError("Network error"))
+    return client
+
+
+@pytest.fixture
+def mock_ai_client_timeout():
+    """模拟超时的 AI 客户端"""
+    client = Mock()
+    client.api_key = "test_key"
+    client.generate = Mock(side_effect=Timeout("Request timeout"))
+    return client
+
+
+@pytest.fixture
+def mock_ai_client_rate_limit():
+    """模拟限流的 AI 客户端"""
+    response = Mock()
+    response.status_code = 429
+    client = Mock()
+    client.api_key = "test_key"
+
+    def generate_side_effect(*args, **kwargs):
+        raise HTTPError(response=response)
+
+    client.generate = Mock(side_effect=generate_side_effect)
+    return client
+
+
+@pytest.fixture
+def mock_ai_client_api_error():
+    """模拟 API 错误的 AI 客户端（不可恢复）"""
+    response = Mock()
+    response.status_code = 400
+    client = Mock()
+    client.api_key = "test_key"
+
+    def generate_side_effect(*args, **kwargs):
+        raise HTTPError(response=response)
+
+    client.generate = Mock(side_effect=generate_side_effect)
+    return client
+
+
+@pytest.fixture
+def mock_ai_client_parse_error():
+    """模拟解析失败的 AI 客户端"""
+    client = Mock()
+    client.api_key = "test_key"
+    # 第一次返回无效 JSON
+    client.generate = Mock(return_value="Invalid JSON response")
+    return client
+
+
+@pytest.fixture
+def mock_ai_client_success():
+    """模拟成功的 AI 客户端"""
+    import json
+    client = Mock()
+    client.api_key = "test_key"
+    client.generate = Mock(return_value=json.dumps({
+        "video_type": "知识类",
+        "segments": [
+            {
+                "title": "测试标题",
+                "content": "测试内容",
+                "start_index": 0,
+                "end_index": 10
+            }
+        ],
+        "actual_segment_count": 1
+    }, ensure_ascii=False))
+    return client
+
+
+@pytest.fixture
+def temp_cache_dir(tmp_path):
+    """临时缓存目录"""
+    cache_dir = tmp_path / "cache" / "semantic"
+    return cache_dir
+
+
+@pytest.fixture
+def semantic_cache(temp_cache_dir):
+    """语义分段缓存实例"""
+    from kb_workflow.srt.cache.semantic_cache import SemanticCache
+    return SemanticCache(cache_dir=temp_cache_dir)
+
+
+@pytest.fixture
+def long_subtitle_segments():
+    """长字幕分段样本（>5000 字符）"""
+    segments = []
+    for i in range(100):
+        segments.append(Segment(
+            id=i + 1,
+            start_time=i * 1.0,
+            end_time=(i + 1) * 1.0,
+            text=f"这是第 {i + 1} 句字幕内容。" * 5
+        ))
+    return segments