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https://github.com/linyqh/NarratoAI.git
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7309208282
更新generate_narration_script.py、base.py和generate_short_summary.py文件，重构解说文案生成和视觉分析器的实现，优先使用新的LLM服务架构。添加回退机制以确保兼容性，增强系统的稳定性和用户体验。
264 lines
7.5 KiB
Python
264 lines
7.5 KiB
Python
#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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"""
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LLM服务测试脚本
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测试新的LLM服务架构是否正常工作
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"""
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import asyncio
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import sys
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import os
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from pathlib import Path
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from loguru import logger
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# 添加项目根目录到Python路径
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project_root = Path(__file__).parent.parent.parent.parent
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sys.path.insert(0, str(project_root))
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from app.services.llm.config_validator import LLMConfigValidator
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from app.services.llm.unified_service import UnifiedLLMService
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from app.services.llm.exceptions import LLMServiceError
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async def test_text_generation():
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"""测试文本生成功能"""
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print("\n🔤 测试文本生成功能...")
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try:
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# 简单的文本生成测试
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prompt = "请用一句话介绍人工智能。"
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result = await UnifiedLLMService.generate_text(
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prompt=prompt,
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system_prompt="你是一个专业的AI助手。",
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temperature=0.7
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)
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print(f"✅ 文本生成成功:")
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print(f" 提示词: {prompt}")
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print(f" 生成结果: {result[:100]}...")
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return True
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except Exception as e:
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print(f"❌ 文本生成失败: {str(e)}")
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return False
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async def test_json_generation():
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"""测试JSON格式生成功能"""
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print("\n📄 测试JSON格式生成功能...")
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try:
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prompt = """
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请生成一个简单的解说文案示例，包含以下字段：
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- title: 标题
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- content: 内容
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- duration: 时长（秒）
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输出JSON格式。
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"""
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result = await UnifiedLLMService.generate_text(
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prompt=prompt,
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system_prompt="你是一个专业的文案撰写专家。",
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temperature=0.7,
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response_format="json"
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)
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# 尝试解析JSON
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import json
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parsed_result = json.loads(result)
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print(f"✅ JSON生成成功:")
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print(f" 生成结果: {json.dumps(parsed_result, ensure_ascii=False, indent=2)}")
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return True
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except json.JSONDecodeError as e:
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print(f"❌ JSON解析失败: {str(e)}")
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print(f" 原始结果: {result}")
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return False
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except Exception as e:
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print(f"❌ JSON生成失败: {str(e)}")
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return False
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async def test_narration_script_generation():
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"""测试解说文案生成功能"""
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print("\n🎬 测试解说文案生成功能...")
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try:
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prompt = """
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根据以下视频描述生成解说文案：
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视频内容：一个人在森林中建造木屋，首先挖掘地基，然后搭建墙壁，最后安装屋顶。
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请生成JSON格式的解说文案，包含items数组，每个item包含：
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- _id: 序号
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- timestamp: 时间戳（格式：HH:MM:SS,mmm-HH:MM:SS,mmm）
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- picture: 画面描述
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- narration: 解说文案
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"""
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result = await UnifiedLLMService.generate_narration_script(
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prompt=prompt,
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temperature=0.8,
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validate_output=True
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)
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print(f"✅ 解说文案生成成功:")
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print(f" 生成了 {len(result)} 个片段")
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for item in result[:2]: # 只显示前2个
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print(f" - {item.get('timestamp', 'N/A')}: {item.get('narration', 'N/A')[:50]}...")
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return True
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except Exception as e:
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print(f"❌ 解说文案生成失败: {str(e)}")
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return False
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async def test_subtitle_analysis():
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"""测试字幕分析功能"""
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print("\n📝 测试字幕分析功能...")
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try:
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subtitle_content = """
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1
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00:00:01,000 --> 00:00:05,000
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大家好，欢迎来到我的频道。
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2
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00:00:05,000 --> 00:00:10,000
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今天我们要学习如何使用人工智能。
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3
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00:00:10,000 --> 00:00:15,000
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人工智能是一项非常有趣的技术。
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"""
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result = await UnifiedLLMService.analyze_subtitle(
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subtitle_content=subtitle_content,
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temperature=0.7,
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validate_output=True
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)
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print(f"✅ 字幕分析成功:")
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print(f" 分析结果: {result[:100]}...")
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return True
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except Exception as e:
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print(f"❌ 字幕分析失败: {str(e)}")
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return False
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def test_config_validation():
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"""测试配置验证功能"""
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print("\n⚙️ 测试配置验证功能...")
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try:
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# 验证所有配置
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validation_results = LLMConfigValidator.validate_all_configs()
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summary = validation_results["summary"]
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print(f"✅ 配置验证完成:")
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print(f" 视觉模型提供商: {summary['valid_vision_providers']}/{summary['total_vision_providers']} 有效")
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print(f" 文本模型提供商: {summary['valid_text_providers']}/{summary['total_text_providers']} 有效")
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if summary["errors"]:
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print(f" 发现 {len(summary['errors'])} 个错误")
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for error in summary["errors"][:3]: # 只显示前3个错误
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print(f" - {error}")
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return summary['valid_text_providers'] > 0
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except Exception as e:
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print(f"❌ 配置验证失败: {str(e)}")
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return False
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def test_provider_info():
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"""测试提供商信息获取"""
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print("\n📋 测试提供商信息获取...")
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try:
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provider_info = UnifiedLLMService.get_provider_info()
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vision_providers = list(provider_info["vision_providers"].keys())
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text_providers = list(provider_info["text_providers"].keys())
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print(f"✅ 提供商信息获取成功:")
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print(f" 视觉模型提供商: {', '.join(vision_providers)}")
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print(f" 文本模型提供商: {', '.join(text_providers)}")
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return True
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except Exception as e:
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print(f"❌ 提供商信息获取失败: {str(e)}")
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return False
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async def run_all_tests():
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"""运行所有测试"""
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print("🚀 开始LLM服务测试...")
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print("="*60)
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# 测试结果统计
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test_results = []
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# 1. 测试配置验证
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test_results.append(("配置验证", test_config_validation()))
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# 2. 测试提供商信息
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test_results.append(("提供商信息", test_provider_info()))
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# 3. 测试文本生成
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test_results.append(("文本生成", await test_text_generation()))
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# 4. 测试JSON生成
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test_results.append(("JSON生成", await test_json_generation()))
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# 5. 测试字幕分析
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test_results.append(("字幕分析", await test_subtitle_analysis()))
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# 6. 测试解说文案生成
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test_results.append(("解说文案生成", await test_narration_script_generation()))
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# 输出测试结果
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print("\n" + "="*60)
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print("📊 测试结果汇总:")
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print("="*60)
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passed = 0
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total = len(test_results)
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for test_name, result in test_results:
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status = "✅ 通过" if result else "❌ 失败"
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print(f" {test_name:<15} {status}")
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if result:
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passed += 1
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print(f"\n总计: {passed}/{total} 个测试通过")
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if passed == total:
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print("🎉 所有测试通过！LLM服务工作正常。")
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elif passed > 0:
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print("⚠️ 部分测试通过，请检查失败的测试项。")
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else:
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print("💥 所有测试失败，请检查配置和网络连接。")
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print("="*60)
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if __name__ == "__main__":
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# 设置日志级别
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logger.remove()
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logger.add(sys.stderr, level="INFO")
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# 运行测试
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asyncio.run(run_all_tests())
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