mirror of https://github.com/Mai-with-u/MaiBot.git
fix:无关紧要的更新
SengokuCola
parent
958d6e04ee
commit
4a074ec374
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@ -227,12 +227,10 @@ class ExpressionSelector:
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return ""
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# 取最后3行作为预测输入
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recent_lines = lines[-3:]
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recent_lines = lines[-1:]
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prediction_input = ' '.join(recent_lines).strip()
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# 如果内容太长，截取前100个字符
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if len(prediction_input) > 100:
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prediction_input = prediction_input[:100]
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logger.info(f"提取预测输入: {prediction_input}")
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return prediction_input
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@ -347,9 +345,6 @@ class ExpressionSelector:
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# 使用模型预测最合适的表达方式
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style_exprs = self.get_model_predicted_expressions(chat_id, chat_info, max_num * 2)
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# if len(style_exprs) < 5:
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# logger.info(f"聊天流 {chat_id} 表达方式正在积累中")
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# return [], []
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# 直接取前max_num个预测结果
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selected_expressions = style_exprs[:max_num]
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@ -1,152 +0,0 @@
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"""
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测试修改后的 expression_selector 使用模型预测功能
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验证不再随机选取，而是使用 style_learner 模型预测
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"""
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import os
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import sys
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import asyncio
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# 添加项目根目录到Python路径
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from src.express.expression_selector import ExpressionSelector
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from src.express.style_learner import style_learner_manager
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from src.common.logger import get_logger
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logger = get_logger("expression_selector_test")
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async def test_model_prediction_selector():
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"""测试使用模型预测的表达选择器"""
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print("=== Expression Selector 模型预测功能测试 ===\n")
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# 创建选择器实例
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selector = ExpressionSelector()
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# 测试聊天室ID
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test_chat_id = "test_prediction_chat"
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print(f"测试聊天室: {test_chat_id}")
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# 1. 先为测试聊天室添加一些风格和situation
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print(f"\n1. 准备测试数据...")
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test_data = [
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("温柔回复", "打招呼"),
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("幽默回复", "表达惊讶"),
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("严肃回复", "询问问题"),
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("活泼回复", "表达开心"),
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("高冷回复", "表示不满"),
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]
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for style, situation in test_data:
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success = style_learner_manager.add_style(test_chat_id, style, situation)
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print(f" 添加: '{style}' (situation: '{situation}') -> {'成功' if success else '失败'}")
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# 2. 学习一些映射关系
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print(f"\n2. 学习映射关系...")
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learning_data = [
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("你好", "温柔回复"),
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("谢谢", "温柔回复"),
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("哈哈", "幽默回复"),
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("请解释", "严肃回复"),
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("太棒了", "活泼回复"),
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]
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for up_content, style in learning_data:
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success = style_learner_manager.learn_mapping(test_chat_id, up_content, style)
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print(f" 学习: '{up_content}' -> '{style}' -> {'成功' if success else '失败'}")
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# 3. 测试模型预测功能
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print(f"\n3. 测试模型预测功能...")
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test_chat_scenarios = [
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"用户: 你好\n机器人: 你好，有什么可以帮助你的吗？",
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"用户: 哈哈，太搞笑了\n机器人: 确实很有趣呢！",
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"用户: 请解释一下这个问题\n机器人: 好的，让我详细说明一下",
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"用户: 太棒了！\n机器人: 很高兴听到这个消息！",
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]
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for i, chat_info in enumerate(test_chat_scenarios, 1):
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print(f"\n 场景 {i}:")
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print(f" 聊天内容: {chat_info}")
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# 使用模型预测表达方式
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predicted_expressions = selector.get_model_predicted_expressions(
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test_chat_id, chat_info, total_num=3
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)
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print(f" 预测结果: {len(predicted_expressions)} 个表达方式")
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for j, expr in enumerate(predicted_expressions, 1):
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print(f" {j}. situation: '{expr['situation']}'")
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print(f" style: '{expr['style']}'")
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print(f" 分数: {expr.get('prediction_score', 0.0):.4f}")
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print(f" 输入: '{expr.get('prediction_input', '')}'")
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# 4. 测试LLM选择功能
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print(f"\n4. 测试LLM选择功能...")
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# 模拟聊天信息
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chat_info = "用户: 你好，我想了解一下这个功能\n机器人: 好的，我来为你详细介绍"
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try:
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selected_expressions, selected_ids = await selector.select_suitable_expressions_llm(
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test_chat_id, chat_info, max_num=3
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)
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print(f" LLM选择结果: {len(selected_expressions)} 个表达方式")
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for i, expr in enumerate(selected_expressions, 1):
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print(f" {i}. situation: '{expr['situation']}'")
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print(f" style: '{expr['style']}'")
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print(f" 来源: {expr['source_id']}")
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except Exception as e:
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print(f" LLM选择失败: {e}")
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# 5. 测试回退机制
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print(f"\n5. 测试回退机制...")
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# 使用不存在的聊天室测试回退
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fake_chat_id = "fake_chat_id"
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fallback_expressions = selector._fallback_random_expressions(fake_chat_id, 3)
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print(f" 回退机制测试: {len(fallback_expressions)} 个表达方式")
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# 6. 测试预测输入提取
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print(f"\n6. 测试预测输入提取...")
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test_chat_infos = [
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"用户: 你好\n机器人: 你好！",
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"这是一段很长的聊天内容，包含了很多信息，用户说了很多话，机器人也回复了很多内容，现在我们要测试提取功能",
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"单行内容",
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"",
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]
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for i, chat_info in enumerate(test_chat_infos, 1):
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prediction_input = selector._extract_prediction_input(chat_info)
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print(f" 测试 {i}:")
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print(f" 原始: '{chat_info}'")
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print(f" 提取: '{prediction_input}'")
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print(f"\n✅ 所有测试完成！")
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print(f"\n=== 功能总结 ===")
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print(f"✓ Expression Selector 现在使用 style_learner 模型进行预测")
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print(f"✓ 不再随机选择，而是基于聊天内容预测最合适的 style")
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print(f"✓ 自动获取预测 style 对应的 situation")
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print(f"✓ 支持多聊天室的预测")
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print(f"✓ 包含回退机制，预测失败时使用随机选择")
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print(f"✓ 支持预测输入提取和优化")
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def main():
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"""主函数"""
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print("Expression Selector 模型预测功能测试")
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print("=" * 60)
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# 运行异步测试
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asyncio.run(test_model_prediction_selector())
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if __name__ == "__main__":
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main()
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