diff --git a/src/chat/affinity_flow/interest_scoring.py b/src/chat/affinity_flow/interest_scoring.py index 1e6a4c6a2..2d2a8f72a 100644 --- a/src/chat/affinity_flow/interest_scoring.py +++ b/src/chat/affinity_flow/interest_scoring.py @@ -49,9 +49,10 @@ class InterestScoringSystem: self, messages: List[DatabaseMessages], bot_nickname: str ) -> List[InterestScore]: """计算消息的兴趣度评分""" - logger.info(f"开始为 {len(messages)} 条消息计算兴趣度...") user_messages = [msg for msg in messages if str(msg.user_info.user_id) != str(global_config.bot.qq_account)] - logger.info(f"正在处理 {len(user_messages)} 条用户消息。") + if not user_messages: + return [] + logger.info(f"正在为 {len(user_messages)} 条用户消息计算兴趣度...") scores = [] for i, msg in enumerate(user_messages, 1): @@ -59,25 +60,18 @@ class InterestScoringSystem: score = await self._calculate_single_message_score(msg, bot_nickname) scores.append(score) - logger.info(f"兴趣度计算完成，共生成 {len(scores)} 个评分。") + logger.info(f"为 {len(scores)} 条消息生成了兴趣度评分。") return scores async def _calculate_single_message_score(self, message: DatabaseMessages, bot_nickname: str) -> InterestScore: """计算单条消息的兴趣度评分""" - logger.info(f"计算消息 {message.message_id} 的分数...") - logger.debug(f"消息长度: {len(message.processed_plain_text)} 字符") + message_preview = f"\033[96m{message.processed_plain_text[:30].replace('\n', ' ')}...\033[0m" + logger.info(f"计算消息 {message.message_id} 的分数 | 内容: {message_preview}") keywords = self._extract_keywords_from_database(message) - logger.debug(f"提取到 {len(keywords)} 个关键词。") - interest_match_score = await self._calculate_interest_match_score(message.processed_plain_text, keywords) - logger.debug(f"兴趣匹配度: {interest_match_score:.3f}") - relationship_score = self._calculate_relationship_score(message.user_info.user_id) - logger.debug(f"关系分数: {relationship_score:.3f}") - mentioned_score = self._calculate_mentioned_score(message, bot_nickname) - logger.debug(f"提及分数: {mentioned_score:.3f}") total_score = ( interest_match_score * self.score_weights["interest_match"] @@ -91,9 +85,10 @@ class InterestScoringSystem: "mentioned": f"提及: {mentioned_score:.3f}", } - logger.info(f"消息 {message.message_id} 最终得分: {total_score:.3f}") - logger.debug(f"Score weights: {self.score_weights}") - logger.debug(f"Score details: {details}") + logger.info( + f"消息 {message.message_id} 得分: {total_score:.3f} " + f"(匹配: {interest_match_score:.2f}, 关系: {relationship_score:.2f}, 提及: {mentioned_score:.2f})" + ) return InterestScore( message_id=message.message_id, @@ -255,18 +250,19 @@ class InterestScoringSystem: return 0.0 # 检查是否被提及 - is_mentioned = msg.is_mentioned or (bot_nickname and bot_nickname in msg.processed_plain_text) + bot_aliases = [bot_nickname] + global_config.bot.alias_names + is_mentioned = msg.is_mentioned or any(alias in msg.processed_plain_text for alias in bot_aliases if alias) # 如果被提及或是私聊，都视为提及了bot - if is_mentioned or not hasattr(msg, "chat_info_group_id"): return global_config.affinity_flow.mention_bot_interest_score return 0.0 - def should_reply(self, score: InterestScore) -> bool: + def should_reply(self, score: InterestScore, message: "DatabaseMessages") -> bool: """判断是否应该回复""" - logger.info(f"评估消息 {score.message_id} (得分: {score.total_score:.3f}) 是否回复...") + message_preview = f"\033[96m{(message.processed_plain_text or 'N/A')[:50].replace('\n', ' ')}\033[0m" + logger.info(f"评估消息 {score.message_id} (得分: {score.total_score:.3f}) | 内容: '{message_preview}...'") base_threshold = self.reply_threshold # 如果被提及，降低阈值 diff --git a/src/chat/interest_system/bot_interest_manager.py b/src/chat/interest_system/bot_interest_manager.py index 4e7702487..9a3e0ec7e 100644 --- a/src/chat/interest_system/bot_interest_manager.py +++ b/src/chat/interest_system/bot_interest_manager.py @@ -404,8 +404,7 @@ class BotInterestManager: if not self.current_interests or not self._initialized: raise RuntimeError("❌ 兴趣标签系统未初始化") - logger.info("开始计算兴趣匹配度...") - logger.debug(f"消息长度: {len(message_text)}, 关键词: {len(keywords) if keywords else 0}") + logger.debug(f"开始计算兴趣匹配度: 消息长度={len(message_text)}, 关键词数={len(keywords) if keywords else 0}") message_id = f"msg_{datetime.now().timestamp()}" result = InterestMatchResult(message_id=message_id) @@ -415,7 +414,7 @@ class BotInterestManager: if not active_tags: raise RuntimeError("没有检测到活跃的兴趣标签") - logger.info(f"正在与 {len(active_tags)} 个兴趣标签进行匹配...") + logger.debug(f"正在与 {len(active_tags)} 个兴趣标签进行匹配...") # 生成消息的embedding logger.debug("正在生成消息 embedding...") @@ -450,9 +449,6 @@ class BotInterestManager: match_count += 1 high_similarity_count += 1 result.add_match(tag.tag_name, enhanced_score, [tag.tag_name]) - logger.debug( - f" 🏷️ '{tag.tag_name}': 相似度={similarity:.3f}, 权重={tag.weight:.2f}, 基础分数={weighted_score:.3f}, 增强分数={enhanced_score:.3f} [高匹配]" - ) elif similarity > medium_threshold: # 中相似度：中等加成 @@ -460,9 +456,6 @@ class BotInterestManager: match_count += 1 medium_similarity_count += 1 result.add_match(tag.tag_name, enhanced_score, [tag.tag_name]) - logger.debug( - f" 🏷️ '{tag.tag_name}': 相似度={similarity:.3f}, 权重={tag.weight:.2f}, 基础分数={weighted_score:.3f}, 增强分数={enhanced_score:.3f} [中匹配]" - ) elif similarity > low_threshold: # 低相似度：轻微加成 @@ -470,9 +463,6 @@ class BotInterestManager: match_count += 1 low_similarity_count += 1 result.add_match(tag.tag_name, enhanced_score, [tag.tag_name]) - logger.debug( - f" 🏷️ '{tag.tag_name}': 相似度={similarity:.3f}, 权重={tag.weight:.2f}, 基础分数={weighted_score:.3f}, 增强分数={enhanced_score:.3f} [低匹配]" - ) logger.info( f"匹配统计: {match_count}/{len(active_tags)} 个标签命中 | " diff --git a/src/chat/planner_actions/planner.py b/src/chat/planner_actions/planner.py index 1657ca050..f1d39610a 100644 --- a/src/chat/planner_actions/planner.py +++ b/src/chat/planner_actions/planner.py @@ -124,7 +124,10 @@ class ActionPlanner: # 3. 根据兴趣度调整可用动作 if interest_scores: latest_score = max(interest_scores, key=lambda s: s.total_score) - should_reply, score = self.interest_scoring.should_reply(latest_score) + latest_message = next( + (msg for msg in unread_messages if msg.message_id == latest_score.message_id), None + ) + should_reply, score = self.interest_scoring.should_reply(latest_score, latest_message) reply_not_available = False if not should_reply and "reply" in initial_plan.available_actions: