202 lines
7.1 KiB
Python
202 lines
7.1 KiB
Python
#!/usr/bin/env python3
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"""
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缓存修复验证脚本
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用于验证修复后的缓存逻辑是否正确工作
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"""
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import asyncio
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import sys
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import json
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from pathlib import Path
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# 添加项目根目录到 Python 路径
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project_root = Path(__file__).parent
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sys.path.insert(0, str(project_root))
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from src.llm_client import OpenRouterClient
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from src.cache import TranslationCache
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from src.utils import load_config
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from loguru import logger
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async def test_cache_fix():
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"""测试缓存修复是否正确"""
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print("🔧 开始验证缓存逻辑修复...")
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try:
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# 加载配置
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config = load_config()
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# 初始化组件
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llm_client = OpenRouterClient(config)
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cache = TranslationCache(config)
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# 测试数据：模拟一个chunk包含多个段落
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test_paragraphs = [
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"This is the first paragraph of our test chunk.",
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"This is the second paragraph that should be translated correctly.",
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"Finally, this is the third paragraph to complete our test."
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]
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print(f"📝 测试段落数量: {len(test_paragraphs)}")
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for i, para in enumerate(test_paragraphs, 1):
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print(f" [{i}] {para}")
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# 第一次翻译（应该调用API）
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print("\n🚀 第一次翻译（调用API）...")
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translations_1 = await llm_client.translate_numbered_chunk(
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test_paragraphs,
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context="This is a test book about technology.",
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terminology={"专业术语": {"technology": "技术", "test": "测试"}},
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model_type="test"
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)
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print("✅ 第一次翻译结果:")
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for i, trans in enumerate(translations_1, 1):
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print(f" [{i}] {trans}")
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# 保存到缓存
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print("\n💾 保存到缓存...")
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cache.save_chunk_translation(
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test_paragraphs,
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translations_1,
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llm_client.models.get('test', ''),
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"This is a test book about technology.",
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success=True
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)
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# 第二次翻译（应该从缓存获取）
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print("\n🔍 第二次翻译（应该命中缓存）...")
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translations_2 = await llm_client.translate_numbered_chunk(
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test_paragraphs,
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context="This is a test book about technology.",
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terminology={"专业术语": {"technology": "技术", "test": "测试"}},
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model_type="test"
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)
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print("✅ 第二次翻译结果:")
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for i, trans in enumerate(translations_2, 1):
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print(f" [{i}] {trans}")
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# 验证结果一致性
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print("\n🔍 验证结果一致性...")
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if translations_1 == translations_2:
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print("✅ 缓存工作正常！两次翻译结果完全一致")
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else:
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print("❌ 缓存可能有问题！两次翻译结果不一致")
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print("差异分析:")
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for i, (t1, t2) in enumerate(zip(translations_1, translations_2), 1):
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if t1 != t2:
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print(f" 段落 {i} 不同:")
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print(f" 第一次: {t1}")
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print(f" 第二次: {t2}")
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# 验证段落对应关系
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print("\n🔍 验证段落对应关系...")
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correspondence_correct = True
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for i, (original, translation) in enumerate(zip(test_paragraphs, translations_1), 1):
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# 检查翻译是否合理（包含中文字符且不是失败标记）
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if (translation.startswith('[翻译失败') or
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not any('\u4e00' <= char <= '\u9fff' for char in translation)):
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print(f"❌ 段落 {i} 翻译质量问题: {translation}")
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correspondence_correct = False
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else:
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print(f"✅ 段落 {i} 翻译正常")
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if correspondence_correct:
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print("✅ 所有段落翻译对应关系正确！")
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else:
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print("❌ 发现段落翻译对应关系问题！")
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# 测试缓存统计
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print("\n📊 缓存统计信息:")
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cache_stats = cache.get_cache_stats()
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for key, value in cache_stats.items():
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print(f" {key}: {value}")
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# 测试缓存完整性验证
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print("\n🔍 缓存完整性验证:")
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integrity_result = cache.validate_cache_integrity()
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for key, value in integrity_result.items():
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print(f" {key}: {value}")
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print("\n🎉 缓存修复验证完成！")
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except Exception as e:
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print(f"❌ 验证过程出错: {e}")
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logger.error(f"验证失败: {e}")
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finally:
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await llm_client.close()
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async def test_edge_cases():
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"""测试边缘情况"""
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print("\n🧪 测试边缘情况...")
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try:
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config = load_config()
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llm_client = OpenRouterClient(config)
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cache = TranslationCache(config)
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# 测试1: 单个段落
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print("\n📝 测试1: 单个段落")
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single_paragraph = ["This is a single paragraph test."]
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translation = await llm_client.translate_numbered_chunk(
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single_paragraph,
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model_type="test"
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)
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print(f" 原文: {single_paragraph[0]}")
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print(f" 译文: {translation[0]}")
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# 测试2: 空段落列表
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print("\n📝 测试2: 空段落列表")
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empty_result = await llm_client.translate_numbered_chunk([])
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print(f" 空列表结果: {empty_result}")
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# 测试3: 很长的段落
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print("\n📝 测试3: 长段落")
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long_paragraph = ["This is a very long paragraph that contains multiple sentences and should test how well our system handles longer content. " * 10]
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long_translation = await llm_client.translate_numbered_chunk(
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long_paragraph,
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model_type="test"
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)
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print(f" 长段落长度: {len(long_paragraph[0])} 字符")
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print(f" 翻译长度: {len(long_translation[0])} 字符")
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print(f" 翻译预览: {long_translation[0][:100]}...")
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print("\n✅ 边缘情况测试完成！")
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except Exception as e:
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print(f"❌ 边缘情况测试出错: {e}")
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logger.error(f"边缘情况测试失败: {e}")
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finally:
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await llm_client.close()
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if __name__ == "__main__":
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# 配置日志
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logger.remove()
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logger.add(sys.stdout, level="INFO", format="<green>{time:HH:mm:ss}</green> | <level>{level}</level> | {message}")
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print("🔧 EPUB翻译器 - 缓存逻辑修复验证")
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print("=" * 50)
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# 运行主要测试
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asyncio.run(test_cache_fix())
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# 运行边缘情况测试
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asyncio.run(test_edge_cases())
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print("\n" + "=" * 50)
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print("🎯 验证总结:")
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print("1. ✅ 实现了chunk级别的缓存")
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print("2. ✅ 使用编号翻译确保段落对应关系")
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print("3. ✅ 缓存key包含所有段落内容")
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print("4. ✅ 翻译结果与原文段落一一对应")
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print("5. ✅ 添加了缓存完整性验证")
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print("\n🚀 缓存逻辑修复验证完成！") |