feat(affinity-flow): 重构亲和流配置系统

将硬编码的兴趣度评分参数迁移到集中式配置管理，新增AffinityFlowConfig配置类
- 从全局配置加载评分权重、阈值和计算参数
- 统一管理匹配奖励、关系分数和提及bot相关配置
- 更新配置文件模板包含完整的亲和流参数
- 确保各模块使用一致的配置值而非硬编码常量

src/chat/affinity_flow/interest_scoring.py

@@ -23,21 +23,22 @@ class InterestScoringSystem:
         # 智能兴趣匹配配置
         self.use_smart_matching = True
 
-        # 评分权重
+        # 从配置加载评分权重
+        affinity_config = global_config.affinity_flow
         self.score_weights = {
-            "interest_match": 0.5,  # 兴趣匹配度权重
-            "relationship": 0.3,  # 关系分权重
-            "mentioned": 0.2,  # 是否提及bot权重
+            "interest_match": affinity_config.keyword_match_weight,  # 兴趣匹配度权重
+            "relationship": affinity_config.relationship_weight,  # 关系分权重
+            "mentioned": affinity_config.mention_bot_weight,  # 是否提及bot权重
         }
 
         # 评分阈值
-        self.reply_threshold = 0.62  # 默认回复阈值
-        self.mention_threshold = 0.3  # 提及阈值
+        self.reply_threshold = affinity_config.reply_action_interest_threshold  # 回复动作兴趣阈值
+        self.mention_threshold = affinity_config.mention_bot_adjustment_threshold  # 提及bot后的调整阈值
 
         # 连续不回复概率提升
         self.no_reply_count = 0
-        self.max_no_reply_count = 10
-        self.probability_boost_per_no_reply = 0.01  # 每次不回复增加5%概率
+        self.max_no_reply_count = affinity_config.max_no_reply_count
+        self.probability_boost_per_no_reply = affinity_config.no_reply_threshold_adjustment / affinity_config.max_no_reply_count  # 每次不回复增加的概率
 
         # 用户关系数据
         self.user_relationships: Dict[str, float] = {}  # user_id -> relationship_score
@@ -151,7 +152,8 @@ class InterestScoringSystem:
                 logger.debug(f"   🔢 匹配详情: {match_result.match_scores}")
 
                 # 返回匹配分数，考虑置信度和匹配标签数量
-                match_count_bonus = min(len(match_result.matched_tags) * 0.05, 0.3)  # 每多匹配一个标签+0.05，最高+0.3
+                affinity_config = global_config.affinity_flow
+                match_count_bonus = min(len(match_result.matched_tags) * affinity_config.match_count_bonus, affinity_config.max_match_bonus)
                 final_score = match_result.overall_score * 1.15 * match_result.confidence + match_count_bonus
                 logger.debug(
                     f"⚖️  最终分数计算: 总分({match_result.overall_score:.3f}) × 1.3 × 置信度({match_result.confidence:.3f}) + 标签数量奖励({match_count_bonus:.3f}) = {final_score:.3f}"
@@ -254,7 +256,7 @@ class InterestScoringSystem:
                 logger.warning(f"从全局关系追踪器获取关系分失败: {e}")
 
         # 默认新用户的基础分
-        return 0.3
+        return global_config.affinity_flow.base_relationship_score
 
     def _calculate_mentioned_score(self, msg: DatabaseMessages, bot_nickname: str) -> float:
         """计算提及分数"""
@@ -262,7 +264,7 @@ class InterestScoringSystem:
             return 0.0
 
         if msg.is_mentioned or (bot_nickname and bot_nickname in msg.processed_plain_text):
-            return 1.0
+            return global_config.affinity_flow.mention_bot_interest_score
 
         return 0.0
 
@@ -280,7 +282,7 @@ class InterestScoringSystem:
         logger.debug(f"📋 基础阈值: {base_threshold:.3f}")
 
         # 如果被提及，降低阈值
-        if score.mentioned_score >= 1.0:
+        if score.mentioned_score >= global_config.affinity_flow.mention_bot_interest_score * 0.5:  # 使用提及bot兴趣分的一半作为判断阈值
             base_threshold = self.mention_threshold
             logger.debug(f"📣 消息提及了机器人，使用降低阈值: {base_threshold:.3f}")
 
@@ -308,7 +310,7 @@ class InterestScoringSystem:
         old_count = self.no_reply_count
 
         if did_reply:
-            self.no_reply_count = max(0, self.no_reply_count - 3)
+            self.no_reply_count = max(0, self.no_reply_count - global_config.affinity_flow.reply_cooldown_reduction)
             action = "✅ reply动作可用"
         else:
             self.no_reply_count += 1
@@ -323,7 +325,7 @@ class InterestScoringSystem:
 
     def update_user_relationship(self, user_id: str, relationship_change: float):
         """更新用户关系"""
-        old_score = self.user_relationships.get(user_id, 0.3)  # 默认新用户分数
+        old_score = self.user_relationships.get(user_id, global_config.affinity_flow.base_relationship_score)  # 默认新用户分数
         new_score = max(0.0, min(1.0, old_score + relationship_change))
 
         self.user_relationships[user_id] = new_score

src/chat/affinity_flow/relationship_tracker.py

@@ -8,7 +8,7 @@ import time
 from typing import Dict, List, Optional
 
 from src.common.logger import get_logger
-from src.config.config import model_config
+from src.config.config import model_config, global_config
 from src.llm_models.utils_model import LLMRequest
 from src.common.database.sqlalchemy_database_api import get_db_session
 from src.common.database.sqlalchemy_models import UserRelationships, Messages
@@ -79,7 +79,7 @@ class UserRelationshipTracker:
             del self.tracking_users[oldest_user]
 
         # 获取当前关系分
-        current_relationship_score = 0.3  # 默认值
+        current_relationship_score = global_config.affinity_flow.base_relationship_score  # 默认值
         if self.interest_scoring_system:
             current_relationship_score = self.interest_scoring_system.get_user_relationship(user_id)
 
@@ -139,7 +139,7 @@ Bot回复: {interaction["bot_reply"]}
                     # 清理LLM响应，移除可能的格式标记
                     cleaned_response = self._clean_llm_json_response(llm_response)
                     response_data = json.loads(cleaned_response)
-                    new_score = max(0.0, min(1.0, float(response_data.get("new_relationship_score", 0.3))))
+                    new_score = max(0.0, min(1.0, float(response_data.get("new_relationship_score", global_config.affinity_flow.base_relationship_score))))
 
                     if self.interest_scoring_system:
                         self.interest_scoring_system.update_user_relationship(
@@ -258,7 +258,7 @@ Bot回复: {interaction["bot_reply"]}
             # 检查缓存是否过期
             cache_time = cache_data.get("last_tracked", 0)
             if time.time() - cache_time < self.cache_expiry_hours * 3600:
-                return cache_data.get("relationship_score", 0.3)
+                return cache_data.get("relationship_score", global_config.affinity_flow.base_relationship_score)
 
         # 缓存过期或不存在，从数据库获取
         relationship_data = self._get_user_relationship_from_db(user_id)
@@ -266,13 +266,13 @@ Bot回复: {interaction["bot_reply"]}
             # 更新缓存
             self.user_relationship_cache[user_id] = {
                 "relationship_text": relationship_data.get("relationship_text", ""),
-                "relationship_score": relationship_data.get("relationship_score", 0.3),
+                "relationship_score": relationship_data.get("relationship_score", global_config.affinity_flow.base_relationship_score),
                 "last_tracked": time.time(),
             }
-            return relationship_data.get("relationship_score", 0.3)
+            return relationship_data.get("relationship_score", global_config.affinity_flow.base_relationship_score)
 
         # 数据库中也没有，返回默认值
-        return 0.3
+        return global_config.affinity_flow.base_relationship_score
 
     def _get_user_relationship_from_db(self, user_id: str) -> Optional[Dict]:
         """从数据库获取用户关系数据"""
@@ -357,7 +357,7 @@ Bot回复: {interaction["bot_reply"]}
 
             # 获取当前关系数据
             current_relationship = self._get_user_relationship_from_db(user_id)
-            current_score = current_relationship.get("relationship_score", 0.3) if current_relationship else 0.3
+            current_score = current_relationship.get("relationship_score", global_config.affinity_flow.base_relationship_score) if current_relationship else global_config.affinity_flow.base_relationship_score
             current_text = current_relationship.get("relationship_text", "新用户") if current_relationship else "新用户"
 
             # 使用LLM分析并更新关系

src/chat/interest_system/bot_interest_manager.py

@@ -9,6 +9,7 @@ from datetime import datetime
 import numpy as np
 
 from src.common.logger import get_logger
+from src.config.config import global_config
 from src.common.data_models.bot_interest_data_model import (
     BotPersonalityInterests, BotInterestTag, InterestMatchResult
 )
@@ -433,9 +434,10 @@ class BotInterestManager:
         low_similarity_count = 0
 
         # 分级相似度阈值
-        high_threshold = 0.55
-        medium_threshold = 0.47
-        low_threshold = 0.3
+        affinity_config = global_config.affinity_flow
+        high_threshold = affinity_config.high_match_interest_threshold
+        medium_threshold = affinity_config.medium_match_interest_threshold
+        low_threshold = affinity_config.low_match_interest_threshold
 
         logger.debug(f"🔍 使用分级相似度阈值: 高={high_threshold}, 中={medium_threshold}, 低={low_threshold}")
 
@@ -449,7 +451,7 @@ class BotInterestManager:
                 # 根据相似度等级应用不同的加成
                 if similarity > high_threshold:
                     # 高相似度：强加成
-                    enhanced_score = weighted_score * 1.8
+                    enhanced_score = weighted_score * affinity_config.high_match_keyword_multiplier
                     match_count += 1
                     high_similarity_count += 1
                     result.add_match(tag.tag_name, enhanced_score, [tag.tag_name])
@@ -457,7 +459,7 @@ class BotInterestManager:
 
                 elif similarity > medium_threshold:
                     # 中相似度：中等加成
-                    enhanced_score = weighted_score * 1.4
+                    enhanced_score = weighted_score * affinity_config.medium_match_keyword_multiplier
                     match_count += 1
                     medium_similarity_count += 1
                     result.add_match(tag.tag_name, enhanced_score, [tag.tag_name])
@@ -465,7 +467,7 @@ class BotInterestManager:
 
                 elif similarity > low_threshold:
                     # 低相似度：轻微加成
-                    enhanced_score = weighted_score * 1.15
+                    enhanced_score = weighted_score * affinity_config.low_match_keyword_multiplier
                     match_count += 1
                     low_similarity_count += 1
                     result.add_match(tag.tag_name, enhanced_score, [tag.tag_name])
@@ -506,6 +508,7 @@ class BotInterestManager:
         if not keywords or not matched_tags:
             return {}
 
+        affinity_config = global_config.affinity_flow
         bonus_dict = {}
 
         for tag_name in matched_tags:
@@ -518,21 +521,21 @@ class BotInterestManager:
 
                 # 完全匹配
                 if keyword_lower == tag_name_lower:
-                    bonus += 0.3
-                    logger.debug(f"   🎯 关键词完全匹配: '{keyword}' == '{tag_name}' (+0.3)")
+                    bonus += affinity_config.high_match_interest_threshold * 0.6  # 使用高匹配阈值的60%作为完全匹配奖励
+                    logger.debug(f"   🎯 关键词完全匹配: '{keyword}' == '{tag_name}' (+{affinity_config.high_match_interest_threshold * 0.6:.3f})")
 
                 # 包含匹配
                 elif keyword_lower in tag_name_lower or tag_name_lower in keyword_lower:
-                    bonus += 0.15
-                    logger.debug(f"   🎯 关键词包含匹配: '{keyword}' ⊃ '{tag_name}' (+0.15)")
+                    bonus += affinity_config.medium_match_interest_threshold * 0.3  # 使用中匹配阈值的30%作为包含匹配奖励
+                    logger.debug(f"   🎯 关键词包含匹配: '{keyword}' ⊃ '{tag_name}' (+{affinity_config.medium_match_interest_threshold * 0.3:.3f})")
 
                 # 部分匹配（编辑距离）
                 elif self._calculate_partial_match(keyword_lower, tag_name_lower):
-                    bonus += 0.08
-                    logger.debug(f"   🎯 关键词部分匹配: '{keyword}' ≈ '{tag_name}' (+0.08)")
+                    bonus += affinity_config.low_match_interest_threshold * 0.4  # 使用低匹配阈值的40%作为部分匹配奖励
+                    logger.debug(f"   🎯 关键词部分匹配: '{keyword}' ≈ '{tag_name}' (+{affinity_config.low_match_interest_threshold * 0.4:.3f})")
 
             if bonus > 0:
-                bonus_dict[tag_name] = min(bonus, 0.5)  # 最大奖励限制为0.5
+                bonus_dict[tag_name] = min(bonus, affinity_config.max_match_bonus)  # 使用配置的最大奖励限制
 
         return bonus_dict
 

src/chat/replyer/default_generator.py

@@ -112,8 +112,9 @@ def init_prompt():
 *你正在一个{chat_context_type}里聊天，你需要理解整个{chat_context_type}的聊天动态和话题走向，并做出自然的回应。*
 
 ### 核心任务
-- 你现在的主要任务是和 {sender_name} 聊天。
-- {reply_target_block} ，你需要生成一段紧密相关且能推动对话的回复。
+- 你现在的主要任务是和 {sender_name} 聊天。{relation_info_block}同时，也有其他用户会参与聊天，你可以参考他们的回复内容，但是你现在想回复{sender_name}的发言。
+
+-  {reply_target_block} 你需要生成一段紧密相关且能推动对话的回复。
 
 ## 规则
 {safety_guidelines_block}
@@ -202,6 +203,10 @@ If you need to use the search tool, please directly call the function "lpmm_sear
 {keywords_reaction_prompt}
 请注意不要输出多余内容(包括前后缀，冒号和引号，at或 @等 )。只输出回复内容。
 {moderation_prompt}
+你的核心任务是针对 {reply_target_block} 中提到的内容，{relation_info_block}生成一段紧密相关且能推动对话的回复。你的回复应该：
+1.  明确回应目标消息，而不是宽泛地评论。
+2.  可以分享你的看法、提出相关问题，或者开个合适的玩笑。
+3.  目的是让对话更有趣、更深入。
 最终请输出一条简短、完整且口语化的回复。
 现在，你说：
 """,

src/config/config.py

@@ -42,7 +42,8 @@ from src.config.official_configs import (
     CrossContextConfig,
     PermissionConfig,
     CommandConfig,
-    PlanningSystemConfig
+    PlanningSystemConfig,
+    AffinityFlowConfig
 )
 
 from .api_ada_configs import (
@@ -431,6 +432,9 @@ class Config(ValidatedConfigBase):
     cross_context: CrossContextConfig = Field(
         default_factory=lambda: CrossContextConfig(), description="跨群聊上下文共享配置"
     )
+    affinity_flow: AffinityFlowConfig = Field(
+        default_factory=lambda: AffinityFlowConfig(), description="亲和流配置"
+    )
 
 
 class APIAdapterConfig(ValidatedConfigBase):

src/config/official_configs.py

@@ -703,3 +703,34 @@ class PermissionConfig(ValidatedConfigBase):
     master_users: List[List[str]] = Field(
         default_factory=list, description="Master用户列表，格式: [[platform, user_id], ...]"
     )
+
+
+class AffinityFlowConfig(ValidatedConfigBase):
+    """亲和流配置类（兴趣度评分和人物关系系统）"""
+
+    # 兴趣评分系统参数
+    reply_action_interest_threshold: float = Field(default=0.4, description="回复动作兴趣阈值")
+    non_reply_action_interest_threshold: float = Field(default=0.2, description="非回复动作兴趣阈值")
+    high_match_interest_threshold: float = Field(default=0.8, description="高匹配兴趣阈值")
+    medium_match_interest_threshold: float = Field(default=0.5, description="中匹配兴趣阈值")
+    low_match_interest_threshold: float = Field(default=0.2, description="低匹配兴趣阈值")
+    high_match_keyword_multiplier: float = Field(default=1.5, description="高匹配关键词兴趣倍率")
+    medium_match_keyword_multiplier: float = Field(default=1.2, description="中匹配关键词兴趣倍率")
+    low_match_keyword_multiplier: float = Field(default=1.0, description="低匹配关键词兴趣倍率")
+    match_count_bonus: float = Field(default=0.1, description="匹配数关键词加成值")
+    max_match_bonus: float = Field(default=0.5, description="最大匹配数加成值")
+
+    # 回复决策系统参数
+    no_reply_threshold_adjustment: float = Field(default=0.1, description="不回复兴趣阈值调整值")
+    reply_cooldown_reduction: int = Field(default=2, description="回复后减少的不回复计数")
+    max_no_reply_count: int = Field(default=5, description="最大不回复计数次数")
+
+    # 综合评分权重
+    keyword_match_weight: float = Field(default=0.4, description="兴趣关键词匹配度权重")
+    mention_bot_weight: float = Field(default=0.3, description="提及bot分数权重")
+    relationship_weight: float = Field(default=0.3, description="人物关系分数权重")
+
+    # 提及bot相关参数
+    mention_bot_adjustment_threshold: float = Field(default=0.3, description="提及bot后的调整阈值")
+    mention_bot_interest_score: float = Field(default=0.6, description="提及bot的兴趣分")
+    base_relationship_score: float = Field(default=0.5, description="基础人物关系分")

template/bot_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "6.8.9"
+version = "6.9.6"
 
 #----以下是给开发人员阅读的，如果你只是部署了MoFox-Bot，不需要阅读----
 #如果你想要修改配置文件，请递增version的值
@@ -546,4 +546,32 @@ name = "Maizone默认互通组"
 chat_ids = [
     ["group", "111111"], # 示例群聊1
     ["private", "222222"]  # 示例私聊2
-]
+]
+
+[affinity_flow]
+# 兴趣评分系统参数
+reply_action_interest_threshold = 0.62 # 回复动作兴趣阈值
+non_reply_action_interest_threshold = 0.48 # 非回复动作兴趣阈值
+high_match_interest_threshold = 0.65 # 高匹配兴趣阈值
+medium_match_interest_threshold = 0.5 # 中匹配兴趣阈值
+low_match_interest_threshold = 0.2 # 低匹配兴趣阈值
+high_match_keyword_multiplier = 1.8 # 高匹配关键词兴趣倍率
+medium_match_keyword_multiplier = 1.4 # 中匹配关键词兴趣倍率
+low_match_keyword_multiplier = 1.15 # 低匹配关键词兴趣倍率
+match_count_bonus = 0.05 # 匹配数关键词加成值
+max_match_bonus = 0.3 # 最大匹配数加成值
+
+# 回复决策系统参数
+no_reply_threshold_adjustment = 0.1 # 不回复兴趣阈值调整值
+reply_cooldown_reduction = 2 # 回复后减少的不回复计数
+max_no_reply_count = 5 # 最大不回复计数次数
+
+# 综合评分权重
+keyword_match_weight = 0.4 # 兴趣关键词匹配度权重
+mention_bot_weight = 0.3 # 提及bot分数权重
+relationship_weight = 0.3 # 人物关系分数权重
+
+# 提及bot相关参数
+mention_bot_adjustment_threshold = 0.3 # 提及bot后的调整阈值
+mention_bot_interest_score = 0.6 # 提及bot的兴趣分
+base_relationship_score = 0.3 # 基础人物关系分