From 4a074ec374fee1ffcbb6a02c8d4cdee4e64d2981 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sat, 11 Oct 2025 20:24:04 +0800 Subject: [PATCH] =?UTF-8?q?fix:=E6=97=A0=E5=85=B3=E7=B4=A7=E8=A6=81?= =?UTF-8?q?=E7=9A=84=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/express/expression_selector.py | 11 +- test_expression_selector_prediction.py | 152 ------------------------- 2 files changed, 3 insertions(+), 160 deletions(-) delete mode 100644 test_expression_selector_prediction.py diff --git a/src/express/expression_selector.py b/src/express/expression_selector.py index d6dfa7cb..cab764c8 100644 --- a/src/express/expression_selector.py +++ b/src/express/expression_selector.py @@ -227,12 +227,10 @@ class ExpressionSelector: return "" # 取最后3行作为预测输入 - recent_lines = lines[-3:] + recent_lines = lines[-1:] prediction_input = ' '.join(recent_lines).strip() - - # 如果内容太长，截取前100个字符 - if len(prediction_input) > 100: - prediction_input = prediction_input[:100] + + logger.info(f"提取预测输入: {prediction_input}") return prediction_input @@ -347,9 +345,6 @@ class ExpressionSelector: # 使用模型预测最合适的表达方式 style_exprs = self.get_model_predicted_expressions(chat_id, chat_info, max_num * 2) - # if len(style_exprs) < 5: - # logger.info(f"聊天流 {chat_id} 表达方式正在积累中") - # return [], [] # 直接取前max_num个预测结果 selected_expressions = style_exprs[:max_num] diff --git a/test_expression_selector_prediction.py b/test_expression_selector_prediction.py deleted file mode 100644 index ef75aa3b..00000000 --- a/test_expression_selector_prediction.py +++ /dev/null @@ -1,152 +0,0 @@ -""" -测试修改后的 expression_selector 使用模型预测功能 -验证不再随机选取，而是使用 style_learner 模型预测 -""" - -import os -import sys -import asyncio - -# 添加项目根目录到Python路径 -sys.path.append(os.path.dirname(os.path.abspath(__file__))) - -from src.express.expression_selector import ExpressionSelector -from src.express.style_learner import style_learner_manager -from src.common.logger import get_logger - -logger = get_logger("expression_selector_test") - - -async def test_model_prediction_selector(): - """测试使用模型预测的表达选择器""" - print("=== Expression Selector 模型预测功能测试 ===\n") - - # 创建选择器实例 - selector = ExpressionSelector() - - # 测试聊天室ID - test_chat_id = "test_prediction_chat" - - print(f"测试聊天室: {test_chat_id}") - - # 1. 先为测试聊天室添加一些风格和situation - print(f"\n1. 准备测试数据...") - - test_data = [ - ("温柔回复", "打招呼"), - ("幽默回复", "表达惊讶"), - ("严肃回复", "询问问题"), - ("活泼回复", "表达开心"), - ("高冷回复", "表示不满"), - ] - - for style, situation in test_data: - success = style_learner_manager.add_style(test_chat_id, style, situation) - print(f" 添加: '{style}' (situation: '{situation}') -> {'成功' if success else '失败'}") - - # 2. 学习一些映射关系 - print(f"\n2. 学习映射关系...") - - learning_data = [ - ("你好", "温柔回复"), - ("谢谢", "温柔回复"), - ("哈哈", "幽默回复"), - ("请解释", "严肃回复"), - ("太棒了", "活泼回复"), - ] - - for up_content, style in learning_data: - success = style_learner_manager.learn_mapping(test_chat_id, up_content, style) - print(f" 学习: '{up_content}' -> '{style}' -> {'成功' if success else '失败'}") - - # 3. 测试模型预测功能 - print(f"\n3. 测试模型预测功能...") - - test_chat_scenarios = [ - "用户: 你好\n机器人: 你好，有什么可以帮助你的吗？", - "用户: 哈哈，太搞笑了\n机器人: 确实很有趣呢！", - "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", - "用户: 太棒了！\n机器人: 很高兴听到这个消息！", - ] - - for i, chat_info in enumerate(test_chat_scenarios, 1): - print(f"\n 场景 {i}:") - print(f" 聊天内容: {chat_info}") - - # 使用模型预测表达方式 - predicted_expressions = selector.get_model_predicted_expressions( - test_chat_id, chat_info, total_num=3 - ) - - print(f" 预测结果: {len(predicted_expressions)} 个表达方式") - for j, expr in enumerate(predicted_expressions, 1): - print(f" {j}. situation: '{expr['situation']}'") - print(f" style: '{expr['style']}'") - print(f" 分数: {expr.get('prediction_score', 0.0):.4f}") - print(f" 输入: '{expr.get('prediction_input', '')}'") - - # 4. 测试LLM选择功能 - print(f"\n4. 测试LLM选择功能...") - - # 模拟聊天信息 - 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" - - try: - selected_expressions, selected_ids = await selector.select_suitable_expressions_llm( - test_chat_id, chat_info, max_num=3 - ) - - print(f" LLM选择结果: {len(selected_expressions)} 个表达方式") - for i, expr in enumerate(selected_expressions, 1): - print(f" {i}. situation: '{expr['situation']}'") - print(f" style: '{expr['style']}'") - print(f" 来源: {expr['source_id']}") - - except Exception as e: - print(f" LLM选择失败: {e}") - - # 5. 测试回退机制 - print(f"\n5. 测试回退机制...") - - # 使用不存在的聊天室测试回退 - fake_chat_id = "fake_chat_id" - fallback_expressions = selector._fallback_random_expressions(fake_chat_id, 3) - print(f" 回退机制测试: {len(fallback_expressions)} 个表达方式") - - # 6. 测试预测输入提取 - print(f"\n6. 测试预测输入提取...") - - test_chat_infos = [ - "用户: 你好\n机器人: 你好！", - "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", - "单行内容", - "", - ] - - for i, chat_info in enumerate(test_chat_infos, 1): - prediction_input = selector._extract_prediction_input(chat_info) - print(f" 测试 {i}:") - print(f" 原始: '{chat_info}'") - print(f" 提取: '{prediction_input}'") - - print(f"\n✅ 所有测试完成！") - print(f"\n=== 功能总结 ===") - print(f"✓ Expression Selector 现在使用 style_learner 模型进行预测") - print(f"✓ 不再随机选择，而是基于聊天内容预测最合适的 style") - print(f"✓ 自动获取预测 style 对应的 situation") - print(f"✓ 支持多聊天室的预测") - print(f"✓ 包含回退机制，预测失败时使用随机选择") - print(f"✓ 支持预测输入提取和优化") - - -def main(): - """主函数""" - print("Expression Selector 模型预测功能测试") - print("=" * 60) - - # 运行异步测试 - asyncio.run(test_model_prediction_selector()) - - -if __name__ == "__main__": - main()