Merge pull request #557 from MaiM-with-u/main-fix

secret 神秘小测验加强版

src/plugins/memory_system/memory_manual_build.py

@@ -23,7 +23,6 @@ import jieba
 root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
 sys.path.append(root_path)
 
-from src.common.logger import get_module_logger # noqa: E402
 from src.common.database import db  # noqa E402
 from src.plugins.memory_system.offline_llm import LLMModel  # noqa E402
 

src/plugins/personality/can_i_recog_u.py

@@ -0,0 +1,351 @@
+"""
+基于聊天记录的人格特征分析系统
+"""
+
+from typing import Dict, List
+import json
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+import sys
+import random
+from collections import defaultdict
+import matplotlib.pyplot as plt
+import numpy as np
+from datetime import datetime
+import matplotlib.font_manager as fm
+
+current_dir = Path(__file__).resolve().parent
+project_root = current_dir.parent.parent.parent
+env_path = project_root / ".env.prod"
+
+root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
+sys.path.append(root_path)
+
+from src.plugins.personality.scene import get_scene_by_factor, PERSONALITY_SCENES  # noqa: E402
+from src.plugins.personality.questionnaire import FACTOR_DESCRIPTIONS  # noqa: E402
+from src.plugins.personality.offline_llm import LLMModel  # noqa: E402
+from src.plugins.personality.who_r_u import MessageAnalyzer  # noqa: E402
+
+# 加载环境变量
+if env_path.exists():
+    print(f"从 {env_path} 加载环境变量")
+    load_dotenv(env_path)
+else:
+    print(f"未找到环境变量文件: {env_path}")
+    print("将使用默认配置")
+
+class ChatBasedPersonalityEvaluator:
+    def __init__(self):
+        self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
+        self.scenarios = []
+        self.message_analyzer = MessageAnalyzer()
+        self.llm = LLMModel()
+        self.trait_scores_history = defaultdict(list)  # 记录每个特质的得分历史
+
+        # 为每个人格特质获取对应的场景
+        for trait in PERSONALITY_SCENES:
+            scenes = get_scene_by_factor(trait)
+            if not scenes:
+                continue
+            scene_keys = list(scenes.keys())
+            selected_scenes = random.sample(scene_keys, min(3, len(scene_keys)))
+            
+            for scene_key in selected_scenes:
+                scene = scenes[scene_key]
+                other_traits = [t for t in PERSONALITY_SCENES if t != trait]
+                secondary_trait = random.choice(other_traits)
+                self.scenarios.append({
+                    "场景": scene["scenario"],
+                    "评估维度": [trait, secondary_trait],
+                    "场景编号": scene_key
+                })
+
+    def analyze_chat_context(self, messages: List[Dict]) -> str:
+        """
+        分析一组消息的上下文，生成场景描述
+        """
+        context = ""
+        for msg in messages:
+            nickname = msg.get('user_info', {}).get('user_nickname', '未知用户')
+            content = msg.get('processed_plain_text', msg.get('detailed_plain_text', ''))
+            if content:
+                context += f"{nickname}: {content}\n"
+        return context
+
+    def evaluate_chat_response(
+        self, user_nickname: str, chat_context: str, dimensions: List[str] = None) -> Dict[str, float]:
+        """
+        评估聊天内容在各个人格维度上的得分
+        """
+        # 使用所有维度进行评估
+        dimensions = list(self.personality_traits.keys())
+        
+        dimension_descriptions = []
+        for dim in dimensions:
+            desc = FACTOR_DESCRIPTIONS.get(dim, "")
+            if desc:
+                dimension_descriptions.append(f"- {dim}：{desc}")
+
+        dimensions_text = "\n".join(dimension_descriptions)
+
+        prompt = f"""请根据以下聊天记录，评估"{user_nickname}"在大五人格模型中的维度得分（1-6分）。
+
+聊天记录：
+{chat_context}
+
+需要评估的维度说明：
+{dimensions_text}
+
+请按照以下格式输出评估结果，注意，你的评价对象是"{user_nickname}"（仅输出JSON格式）：
+{{
+    "开放性": 分数,
+    "严谨性": 分数,
+    "外向性": 分数,
+    "宜人性": 分数,
+    "神经质": 分数
+}}
+
+评分标准：
+1 = 非常不符合该维度特征
+2 = 比较不符合该维度特征
+3 = 有点不符合该维度特征
+4 = 有点符合该维度特征
+5 = 比较符合该维度特征
+6 = 非常符合该维度特征
+
+如果你觉得某个维度没有相关信息或者无法判断，请输出0分
+
+请根据聊天记录的内容和语气，结合维度说明进行评分。如果维度可以评分，确保分数在1-6之间。如果没有体现，请输出0分"""
+
+        try:
+            ai_response, _ = self.llm.generate_response(prompt)
+            start_idx = ai_response.find("{")
+            end_idx = ai_response.rfind("}") + 1
+            if start_idx != -1 and end_idx != 0:
+                json_str = ai_response[start_idx:end_idx]
+                scores = json.loads(json_str)
+                return {k: max(0, min(6, float(v))) for k, v in scores.items()}
+            else:
+                print("AI响应格式不正确，使用默认评分")
+                return {dim: 0 for dim in dimensions}
+        except Exception as e:
+            print(f"评估过程出错：{str(e)}")
+            return {dim: 0 for dim in dimensions}
+
+    def evaluate_user_personality(self, qq_id: str, num_samples: int = 10, context_length: int = 5) -> Dict:
+        """
+        基于用户的聊天记录评估人格特征
+        
+        Args:
+            qq_id (str): 用户QQ号
+            num_samples (int): 要分析的聊天片段数量
+            context_length (int): 每个聊天片段的上下文长度
+            
+        Returns:
+            Dict: 评估结果
+        """
+        # 获取用户的随机消息及其上下文
+        chat_contexts, user_nickname = self.message_analyzer.get_user_random_contexts(
+            qq_id, num_messages=num_samples, context_length=context_length)
+        if not chat_contexts:
+            return {"error": f"没有找到QQ号 {qq_id} 的消息记录"}
+
+        # 初始化评分
+        final_scores = defaultdict(float)
+        dimension_counts = defaultdict(int)
+        chat_samples = []
+        
+        # 清空历史记录
+        self.trait_scores_history.clear()
+
+        # 分析每个聊天上下文
+        for chat_context in chat_contexts:
+            # 评估这段聊天内容的所有维度
+            scores = self.evaluate_chat_response(user_nickname, chat_context)
+            
+            # 记录样本
+            chat_samples.append({
+                "聊天内容": chat_context,
+                "评估维度": list(self.personality_traits.keys()),
+                "评分": scores
+            })
+
+            # 更新总分和历史记录
+            for dimension, score in scores.items():
+                if score > 0:  # 只统计大于0的有效分数
+                    final_scores[dimension] += score
+                    dimension_counts[dimension] += 1
+                self.trait_scores_history[dimension].append(score)
+
+        # 计算平均分
+        average_scores = {}
+        for dimension in self.personality_traits:
+            if dimension_counts[dimension] > 0:
+                average_scores[dimension] = round(final_scores[dimension] / dimension_counts[dimension], 2)
+            else:
+                average_scores[dimension] = 0  # 如果没有有效分数，返回0
+
+        # 生成趋势图
+        self._generate_trend_plot(qq_id, user_nickname)
+
+        result = {
+            "用户QQ": qq_id,
+            "用户昵称": user_nickname,
+            "样本数量": len(chat_samples),
+            "人格特征评分": average_scores,
+            "维度评估次数": dict(dimension_counts),
+            "详细样本": chat_samples,
+            "特质得分历史": {k: v for k, v in self.trait_scores_history.items()}
+        }
+
+        # 保存结果
+        os.makedirs("results", exist_ok=True)
+        result_file = f"results/personality_result_{qq_id}.json"
+        with open(result_file, "w", encoding="utf-8") as f:
+            json.dump(result, f, ensure_ascii=False, indent=2)
+
+        return result
+
+    def _generate_trend_plot(self, qq_id: str, user_nickname: str):
+        """
+        生成人格特质累计平均分变化趋势图
+        """
+        # 查找系统中可用的中文字体
+        chinese_fonts = []
+        for f in fm.fontManager.ttflist:
+            try:
+                if '简' in f.name or 'SC' in f.name or '黑' in f.name or '宋' in f.name or '微软' in f.name:
+                    chinese_fonts.append(f.name)
+            except Exception:
+                continue
+        
+        if chinese_fonts:
+            plt.rcParams['font.sans-serif'] = chinese_fonts + ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS']
+        else:
+            # 如果没有找到中文字体，使用默认字体，并将中文昵称转换为拼音或英文
+            try:
+                from pypinyin import lazy_pinyin
+                user_nickname = ''.join(lazy_pinyin(user_nickname))
+            except ImportError:
+                user_nickname = "User"  # 如果无法转换为拼音，使用默认英文
+                
+        plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题
+        
+        plt.figure(figsize=(12, 6))
+        plt.style.use('bmh')  # 使用内置的bmh样式，它有类似seaborn的美观效果
+        
+        colors = {
+            "开放性": "#FF9999",
+            "严谨性": "#66B2FF",
+            "外向性": "#99FF99",
+            "宜人性": "#FFCC99",
+            "神经质": "#FF99CC"
+        }
+        
+        # 计算每个维度在每个时间点的累计平均分
+        cumulative_averages = {}
+        for trait, scores in self.trait_scores_history.items():
+            if not scores:
+                continue
+                
+            averages = []
+            total = 0
+            valid_count = 0
+            for score in scores:
+                if score > 0:  # 只计算大于0的有效分数
+                    total += score
+                    valid_count += 1
+                    if valid_count > 0:
+                        averages.append(total / valid_count)
+                else:
+                    # 如果当前分数无效，使用前一个有效的平均分
+                    if averages:
+                        averages.append(averages[-1])
+                    else:
+                        continue  # 跳过无效分数
+                        
+            if averages:  # 只有在有有效分数的情况下才添加到累计平均中
+                cumulative_averages[trait] = averages
+        
+        # 绘制每个维度的累计平均分变化趋势
+        for trait, averages in cumulative_averages.items():
+            x = range(1, len(averages) + 1)
+            plt.plot(x, averages, 'o-', label=trait, color=colors.get(trait), linewidth=2, markersize=8)
+            
+            # 添加趋势线
+            z = np.polyfit(x, averages, 1)
+            p = np.poly1d(z)
+            plt.plot(x, p(x), '--', color=colors.get(trait), alpha=0.5)
+
+        plt.title(f"{user_nickname} 的人格特质累计平均分变化趋势", fontsize=14, pad=20)
+        plt.xlabel("评估次数", fontsize=12)
+        plt.ylabel("累计平均分", fontsize=12)
+        plt.grid(True, linestyle='--', alpha=0.7)
+        plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+        plt.ylim(0, 7)
+        plt.tight_layout()
+
+        # 保存图表
+        os.makedirs("results/plots", exist_ok=True)
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        plot_file = f"results/plots/personality_trend_{qq_id}_{timestamp}.png"
+        plt.savefig(plot_file, dpi=300, bbox_inches='tight')
+        plt.close()
+
+def analyze_user_personality(qq_id: str, num_samples: int = 10, context_length: int = 5) -> str:
+    """
+    分析用户人格特征的便捷函数
+    
+    Args:
+        qq_id (str): 用户QQ号
+        num_samples (int): 要分析的聊天片段数量
+        context_length (int): 每个聊天片段的上下文长度
+    
+    Returns:
+        str: 格式化的分析结果
+    """
+    evaluator = ChatBasedPersonalityEvaluator()
+    result = evaluator.evaluate_user_personality(qq_id, num_samples, context_length)
+    
+    if "error" in result:
+        return result["error"]
+    
+    # 格式化输出
+    output = f"QQ号 {qq_id} ({result['用户昵称']}) 的人格特征分析结果：\n"
+    output += "=" * 50 + "\n\n"
+    
+    output += "人格特征评分：\n"
+    for trait, score in result["人格特征评分"].items():
+        if score == 0:
+            output += f"{trait}: 数据不足，无法判断 (评估次数: {result['维度评估次数'].get(trait, 0)})\n"
+        else:
+            output += f"{trait}: {score}/6 (评估次数: {result['维度评估次数'].get(trait, 0)})\n"
+        
+        # 添加变化趋势描述
+        if trait in result["特质得分历史"] and len(result["特质得分历史"][trait]) > 1:
+            scores = [s for s in result["特质得分历史"][trait] if s != 0]  # 过滤掉无效分数
+            if len(scores) > 1:  # 确保有足够的有效分数计算趋势
+                trend = np.polyfit(range(len(scores)), scores, 1)[0]
+                if abs(trend) < 0.1:
+                    trend_desc = "保持稳定"
+                elif trend > 0:
+                    trend_desc = "呈上升趋势"
+                else:
+                    trend_desc = "呈下降趋势"
+                output += f"    变化趋势: {trend_desc} (斜率: {trend:.2f})\n"
+    
+    output += f"\n分析样本数量：{result['样本数量']}\n"
+    output += f"结果已保存至：results/personality_result_{qq_id}.json\n"
+    output += "变化趋势图已保存至：results/plots/目录\n"
+    
+    return output
+
+if __name__ == "__main__":
+    # 测试代码
+    # test_qq = ""  # 替换为要测试的QQ号
+    # print(analyze_user_personality(test_qq, num_samples=30, context_length=20))
+    # test_qq = ""
+    # print(analyze_user_personality(test_qq, num_samples=30, context_length=20))
+    test_qq = "1026294844"
+    print(analyze_user_personality(test_qq, num_samples=30, context_length=30))

src/plugins/personality/renqingziji_with_mymy.py

@@ -0,0 +1,196 @@
+"""
+The definition of artificial personality in this paper follows the dispositional para-digm and adapts a definition of
+personality developed for humans [17]:
+Personality for a human is the "whole and organisation of relatively stable tendencies and patterns of experience and
+behaviour within one person (distinguishing it from other persons)". This definition is modified for artificial
+personality:
+Artificial personality describes the relatively stable tendencies and patterns of behav-iour of an AI-based machine that
+can be designed by developers and designers via different modalities, such as language, creating the impression
+of individuality of a humanized social agent when users interact with the machine."""
+
+from typing import Dict, List
+import json
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+import sys
+
+"""
+第一种方案：基于情景评估的人格测定
+"""
+current_dir = Path(__file__).resolve().parent
+project_root = current_dir.parent.parent.parent
+env_path = project_root / ".env.prod"
+
+root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
+sys.path.append(root_path)
+
+from src.plugins.personality.scene import get_scene_by_factor, PERSONALITY_SCENES  # noqa: E402
+from src.plugins.personality.questionnaire import FACTOR_DESCRIPTIONS  # noqa: E402
+from src.plugins.personality.offline_llm import LLMModel  # noqa: E402
+
+# 加载环境变量
+if env_path.exists():
+    print(f"从 {env_path} 加载环境变量")
+    load_dotenv(env_path)
+else:
+    print(f"未找到环境变量文件: {env_path}")
+    print("将使用默认配置")
+
+
+class PersonalityEvaluator_direct:
+    def __init__(self):
+        self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
+        self.scenarios = []
+
+        # 为每个人格特质获取对应的场景
+        for trait in PERSONALITY_SCENES:
+            scenes = get_scene_by_factor(trait)
+            if not scenes:
+                continue
+
+            # 从每个维度选择3个场景
+            import random
+
+            scene_keys = list(scenes.keys())
+            selected_scenes = random.sample(scene_keys, min(3, len(scene_keys)))
+
+            for scene_key in selected_scenes:
+                scene = scenes[scene_key]
+
+                # 为每个场景添加评估维度
+                # 主维度是当前特质，次维度随机选择一个其他特质
+                other_traits = [t for t in PERSONALITY_SCENES if t != trait]
+                secondary_trait = random.choice(other_traits)
+
+                self.scenarios.append(
+                    {"场景": scene["scenario"], "评估维度": [trait, secondary_trait], "场景编号": scene_key}
+                )
+
+        self.llm = LLMModel()
+
+    def evaluate_response(self, scenario: str, response: str, dimensions: List[str]) -> Dict[str, float]:
+        """
+        使用 DeepSeek AI 评估用户对特定场景的反应
+        """
+        # 构建维度描述
+        dimension_descriptions = []
+        for dim in dimensions:
+            desc = FACTOR_DESCRIPTIONS.get(dim, "")
+            if desc:
+                dimension_descriptions.append(f"- {dim}：{desc}")
+
+        dimensions_text = "\n".join(dimension_descriptions)
+
+
+        prompt = f"""请根据以下场景和用户描述，评估用户在大五人格模型中的相关维度得分（1-6分）。
+
+场景描述：
+{scenario}
+
+用户回应：
+{response}
+
+需要评估的维度说明：
+{dimensions_text}
+
+请按照以下格式输出评估结果（仅输出JSON格式）：
+{{
+    "{dimensions[0]}": 分数,
+    "{dimensions[1]}": 分数
+}}
+
+评分标准：
+1 = 非常不符合该维度特征
+2 = 比较不符合该维度特征
+3 = 有点不符合该维度特征
+4 = 有点符合该维度特征
+5 = 比较符合该维度特征
+6 = 非常符合该维度特征
+
+请根据用户的回应，结合场景和维度说明进行评分。确保分数在1-6之间，并给出合理的评估。"""
+
+        try:
+            ai_response, _ = self.llm.generate_response(prompt)
+            # 尝试从AI响应中提取JSON部分
+            start_idx = ai_response.find("{")
+            end_idx = ai_response.rfind("}") + 1
+            if start_idx != -1 and end_idx != 0:
+                json_str = ai_response[start_idx:end_idx]
+                scores = json.loads(json_str)
+                # 确保所有分数在1-6之间
+                return {k: max(1, min(6, float(v))) for k, v in scores.items()}
+            else:
+                print("AI响应格式不正确，使用默认评分")
+                return {dim: 3.5 for dim in dimensions}
+        except Exception as e:
+            print(f"评估过程出错：{str(e)}")
+            return {dim: 3.5 for dim in dimensions}
+
+
+def main():
+    print("欢迎使用人格形象创建程序！")
+    print("接下来，您将面对一系列场景（共15个）。请根据您想要创建的角色形象，描述在该场景下可能的反应。")
+    print("每个场景都会评估不同的人格维度，最终得出完整的人格特征评估。")
+    print("评分标准：1=非常不符合，2=比较不符合，3=有点不符合，4=有点符合，5=比较符合，6=非常符合")
+    print("\n准备好了吗？按回车键开始...")
+    input()
+
+    evaluator = PersonalityEvaluator_direct()
+    final_scores = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
+    dimension_counts = {trait: 0 for trait in final_scores.keys()}
+
+    for i, scenario_data in enumerate(evaluator.scenarios, 1):
+        print(f"\n场景 {i}/{len(evaluator.scenarios)} - {scenario_data['场景编号']}:")
+        print("-" * 50)
+        print(scenario_data["场景"])
+        print("\n请描述您的角色在这种情况下会如何反应：")
+        response = input().strip()
+
+        if not response:
+            print("反应描述不能为空！")
+            continue
+
+        print("\n正在评估您的描述...")
+        scores = evaluator.evaluate_response(scenario_data["场景"], response, scenario_data["评估维度"])
+
+        # 更新最终分数
+        for dimension, score in scores.items():
+            final_scores[dimension] += score
+            dimension_counts[dimension] += 1
+
+        print("\n当前评估结果：")
+        print("-" * 30)
+        for dimension, score in scores.items():
+            print(f"{dimension}: {score}/6")
+
+        if i < len(evaluator.scenarios):
+            print("\n按回车键继续下一个场景...")
+            input()
+
+    # 计算平均分
+    for dimension in final_scores:
+        if dimension_counts[dimension] > 0:
+            final_scores[dimension] = round(final_scores[dimension] / dimension_counts[dimension], 2)
+
+    print("\n最终人格特征评估结果：")
+    print("-" * 30)
+    for trait, score in final_scores.items():
+        print(f"{trait}: {score}/6")
+        print(f"测试场景数：{dimension_counts[trait]}")
+
+    # 保存结果
+    result = {"final_scores": final_scores, "dimension_counts": dimension_counts, "scenarios": evaluator.scenarios}
+
+    # 确保目录存在
+    os.makedirs("results", exist_ok=True)
+
+    # 保存到文件
+    with open("results/personality_result.json", "w", encoding="utf-8") as f:
+        json.dump(result, f, ensure_ascii=False, indent=2)
+
+    print("\n结果已保存到 results/personality_result.json")
+
+
+if __name__ == "__main__":
+    main()

src/plugins/personality/who_r_u.py

@@ -0,0 +1,155 @@
+import random
+import os
+import sys
+from pathlib import Path
+import datetime
+from typing import List, Dict, Optional
+
+current_dir = Path(__file__).resolve().parent
+project_root = current_dir.parent.parent.parent
+env_path = project_root / ".env.prod"
+
+root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
+sys.path.append(root_path)
+
+from src.common.database import db  # noqa: E402
+
+class MessageAnalyzer:
+    def __init__(self):
+        self.messages_collection = db["messages"]
+    
+    def get_message_context(self, message_id: int, context_length: int = 5) -> Optional[List[Dict]]:
+        """
+        获取指定消息ID的上下文消息列表
+        
+        Args:
+            message_id (int): 消息ID
+            context_length (int): 上下文长度（单侧，总长度为 2*context_length + 1）
+            
+        Returns:
+            Optional[List[Dict]]: 消息列表，如果未找到则返回None
+        """
+        # 从数据库获取指定消息
+        target_message = self.messages_collection.find_one({"message_id": message_id})
+        if not target_message:
+            return None
+            
+        # 获取该消息的stream_id
+        stream_id = target_message.get('chat_info', {}).get('stream_id')
+        if not stream_id:
+            return None
+            
+        # 获取同一stream_id的所有消息
+        stream_messages = list(self.messages_collection.find({
+            "chat_info.stream_id": stream_id
+        }).sort("time", 1))
+        
+        # 找到目标消息在列表中的位置
+        target_index = None
+        for i, msg in enumerate(stream_messages):
+            if msg['message_id'] == message_id:
+                target_index = i
+                break
+                
+        if target_index is None:
+            return None
+            
+        # 获取目标消息前后的消息
+        start_index = max(0, target_index - context_length)
+        end_index = min(len(stream_messages), target_index + context_length + 1)
+        
+        return stream_messages[start_index:end_index]
+    
+    def format_messages(self, messages: List[Dict], target_message_id: Optional[int] = None) -> str:
+        """
+        格式化消息列表为可读字符串
+        
+        Args:
+            messages (List[Dict]): 消息列表
+            target_message_id (Optional[int]): 目标消息ID，用于标记
+            
+        Returns:
+            str: 格式化的消息字符串
+        """
+        if not messages:
+            return "没有消息记录"
+            
+        reply = ""
+        for msg in messages:
+            # 消息时间
+            msg_time = datetime.datetime.fromtimestamp(int(msg['time'])).strftime("%Y-%m-%d %H:%M:%S")
+            
+            # 获取消息内容
+            message_text = msg.get('processed_plain_text', msg.get('detailed_plain_text', '无消息内容'))
+            nickname = msg.get('user_info', {}).get('user_nickname', '未知用户')
+            
+            # 标记当前消息
+            is_target = "→ " if target_message_id and msg['message_id'] == target_message_id else "  "
+            
+            reply += f"{is_target}[{msg_time}] {nickname}: {message_text}\n"
+            
+            if target_message_id and msg['message_id'] == target_message_id:
+                reply += "  " + "-" * 50 + "\n"
+                
+        return reply
+    
+    def get_user_random_contexts(
+        self, qq_id: str, num_messages: int = 10, context_length: int = 5) -> tuple[List[str], str]:  # noqa: E501
+        """
+        获取用户的随机消息及其上下文
+        
+        Args:
+            qq_id (str): QQ号
+            num_messages (int): 要获取的随机消息数量
+            context_length (int): 每条消息的上下文长度（单侧）
+            
+        Returns:
+            tuple[List[str], str]: (每个消息上下文的格式化字符串列表, 用户昵称)
+        """
+        if not qq_id:
+            return [], ""
+        
+        # 获取用户所有消息
+        all_messages = list(self.messages_collection.find({"user_info.user_id": int(qq_id)}))
+        if not all_messages:
+            return [], ""
+        
+        # 获取用户昵称
+        user_nickname = all_messages[0].get('chat_info', {}).get('user_info', {}).get('user_nickname', '未知用户')
+        
+        # 随机选择指定数量的消息
+        selected_messages = random.sample(all_messages, min(num_messages, len(all_messages)))
+        # 按时间排序
+        selected_messages.sort(key=lambda x: int(x['time']))
+        
+        # 存储所有上下文消息
+        context_list = []
+        
+        # 获取每条消息的上下文
+        for msg in selected_messages:
+            message_id = msg['message_id']
+            
+            # 获取消息上下文
+            context_messages = self.get_message_context(message_id, context_length)
+            if context_messages:
+                formatted_context = self.format_messages(context_messages, message_id)
+                context_list.append(formatted_context)
+        
+        return context_list, user_nickname
+
+if __name__ == "__main__":
+    # 测试代码
+    analyzer = MessageAnalyzer()
+    test_qq = "1026294844"  # 替换为要测试的QQ号
+    print(f"测试QQ号: {test_qq}")
+    print("-" * 50)
+    # 获取5条消息，每条消息前后各3条上下文
+    contexts, nickname = analyzer.get_user_random_contexts(test_qq, num_messages=5, context_length=3)
+    
+    print(f"用户昵称: {nickname}\n")
+    # 打印每个上下文
+    for i, context in enumerate(contexts, 1):
+        print(f"\n随机消息 {i}/{len(contexts)}:")
+        print("-" * 30)
+        print(context)
+        print("=" * 50)

src/plugins/willing/mode_classical.py

@@ -41,8 +41,8 @@ class WillingManager:
 
         interested_rate = interested_rate * config.response_interested_rate_amplifier
 
-        if interested_rate > 0.5:
+        if interested_rate > 0.4:
-            current_willing += interested_rate - 0.5
+            current_willing += interested_rate - 0.3
 
         if is_mentioned_bot and current_willing < 1.0:
             current_willing += 1