feat(affinity-flow): 优化兴趣度评分和回复决策逻辑
- 降低回复阈值从0.6到0.55以增加回复可能性 - 在最终分数计算中加入标签数量奖励机制，每多匹配一个标签加0.05分，最高加0.3分 - 引入分级相似度匹配系统（高/中/低）并应用不同加成系数 - 增加关键词直接匹配奖励机制，支持完全匹配、包含匹配和部分匹配 - 在计划过滤器中处理回复动作不可用时的自动转换逻辑 - 增加兴趣度阈值80%检查，低于该阈值直接返回no_action - 优化日志输出和统计信息，提供更详细的匹配分析
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Windpicker-owo
@@ -429,26 +429,64 @@ class BotInterestManager:
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# 计算与每个兴趣标签的相似度
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match_count = 0
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high_similarity_count = 0
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similarity_threshold = 0.3
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medium_similarity_count = 0
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low_similarity_count = 0
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logger.debug(f"🔍 使用相似度阈值: {similarity_threshold}")
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# 分级相似度阈值
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high_threshold = 0.5
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medium_threshold = 0.3
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low_threshold = 0.15
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logger.debug(f"🔍 使用分级相似度阈值: 高={high_threshold}, 中={medium_threshold}, 低={low_threshold}")
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for tag in active_tags:
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if tag.embedding:
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similarity = self._calculate_cosine_similarity(message_embedding, tag.embedding)
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# 基础加权分数
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weighted_score = similarity * tag.weight
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if similarity > similarity_threshold:
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# 根据相似度等级应用不同的加成
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if similarity > high_threshold:
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# 高相似度：强加成
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enhanced_score = weighted_score * 1.5
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match_count += 1
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result.add_match(tag.tag_name, weighted_score, [tag.tag_name])
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high_similarity_count += 1
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result.add_match(tag.tag_name, enhanced_score, [tag.tag_name])
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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]")
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if similarity > 0.7:
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high_similarity_count += 1
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elif similarity > medium_threshold:
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# 中相似度：中等加成
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enhanced_score = weighted_score * 1.2
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match_count += 1
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medium_similarity_count += 1
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result.add_match(tag.tag_name, enhanced_score, [tag.tag_name])
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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]")
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logger.debug(f" 🏷️ '{tag.tag_name}': 相似度={similarity:.3f}, 权重={tag.weight:.2f}, 加权分数={weighted_score:.3f}")
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elif similarity > low_threshold:
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# 低相似度：轻微加成
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enhanced_score = weighted_score * 1.05
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match_count += 1
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low_similarity_count += 1
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result.add_match(tag.tag_name, enhanced_score, [tag.tag_name])
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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]")
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logger.info(f"📈 匹配统计: {match_count}/{len(active_tags)} 个标签超过阈值")
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logger.info(f"🔥 高相似度匹配(>0.7): {high_similarity_count} 个")
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logger.info(f"🔥 高相似度匹配(>{high_threshold}): {high_similarity_count} 个")
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logger.info(f"⚡ 中相似度匹配(>{medium_threshold}): {medium_similarity_count} 个")
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logger.info(f"🌊 低相似度匹配(>{low_threshold}): {low_similarity_count} 个")
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# 添加直接关键词匹配奖励
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keyword_bonus = self._calculate_keyword_match_bonus(keywords, result.matched_tags)
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logger.debug(f"🎯 关键词直接匹配奖励: {keyword_bonus}")
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# 应用关键词奖励到匹配分数
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for tag_name in result.matched_tags:
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if tag_name in keyword_bonus:
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original_score = result.match_scores[tag_name]
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bonus = keyword_bonus[tag_name]
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result.match_scores[tag_name] = original_score + bonus
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logger.debug(f" 🏷️ '{tag_name}': 原始分数={original_score:.3f}, 奖励={bonus:.3f}, 最终分数={result.match_scores[tag_name]:.3f}")
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# 计算总体分数
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result.calculate_overall_score()
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@@ -463,6 +501,79 @@ class BotInterestManager:
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return result
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def _calculate_keyword_match_bonus(self, keywords: List[str], matched_tags: List[str]) -> Dict[str, float]:
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"""计算关键词直接匹配奖励"""
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if not keywords or not matched_tags:
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return {}
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bonus_dict = {}
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for tag_name in matched_tags:
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bonus = 0.0
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# 检查关键词与标签的直接匹配
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for keyword in keywords:
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keyword_lower = keyword.lower().strip()
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tag_name_lower = tag_name.lower()
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# 完全匹配
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if keyword_lower == tag_name_lower:
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bonus += 0.3
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logger.debug(f" 🎯 关键词完全匹配: '{keyword}' == '{tag_name}' (+0.3)")
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# 包含匹配
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elif keyword_lower in tag_name_lower or tag_name_lower in keyword_lower:
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bonus += 0.15
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logger.debug(f" 🎯 关键词包含匹配: '{keyword}' ⊃ '{tag_name}' (+0.15)")
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# 部分匹配（编辑距离）
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elif self._calculate_partial_match(keyword_lower, tag_name_lower):
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bonus += 0.08
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logger.debug(f" 🎯 关键词部分匹配: '{keyword}' ≈ '{tag_name}' (+0.08)")
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if bonus > 0:
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bonus_dict[tag_name] = min(bonus, 0.5) # 最大奖励限制为0.5
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return bonus_dict
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def _calculate_partial_match(self, text1: str, text2: str) -> bool:
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"""计算部分匹配（基于编辑距离）"""
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try:
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# 简单的编辑距离计算
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max_len = max(len(text1), len(text2))
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if max_len == 0:
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return False
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# 计算编辑距离
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distance = self._levenshtein_distance(text1, text2)
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# 如果编辑距离小于较短字符串长度的一半，认为是部分匹配
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min_len = min(len(text1), len(text2))
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return distance <= min_len // 2
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except Exception:
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return False
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def _levenshtein_distance(self, s1: str, s2: str) -> int:
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"""计算莱文斯坦距离"""
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if len(s1) < len(s2):
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return self._levenshtein_distance(s2, s1)
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if len(s2) == 0:
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return len(s1)
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previous_row = range(len(s2) + 1)
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for i, c1 in enumerate(s1):
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current_row = [i + 1]
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for j, c2 in enumerate(s2):
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insertions = previous_row[j + 1] + 1
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deletions = current_row[j] + 1
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substitutions = previous_row[j] + (c1 != c2)
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current_row.append(min(insertions, deletions, substitutions))
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previous_row = current_row
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return previous_row[-1]
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def _calculate_cosine_similarity(self, vec1: List[float], vec2: List[float]) -> float:
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"""计算余弦相似度"""
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try:
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