refactor(scoring): 重构评分系统使用统一API替代插件内部实现
将原有的兴趣度评分系统重构为使用统一的评分API，移除了插件内部实现并更新了所有相关引用。主要变更包括： - 替换 chatter_interest_scoring_system 为 scoring_api - 移除 interest_scoring.py 文件，统一使用 src/plugin_system/apis/scoring_api - 更新关系追踪器以使用统一API，保持向后兼容性 - 简化了多个模块中的关系分获取逻辑
This commit is contained in:
Windpicker-owo
@@ -1,333 +0,0 @@
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"""
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兴趣度评分系统
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基于多维度评分机制，包括兴趣匹配度、用户关系分、提及度和时间因子
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现在使用embedding计算智能兴趣匹配
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"""
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import traceback
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from typing import Any
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from src.chat.interest_system import bot_interest_manager
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from src.common.data_models.database_data_model import DatabaseMessages
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from src.common.data_models.info_data_model import InterestScore
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from src.common.logger import get_logger
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from src.config.config import global_config
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logger = get_logger("chatter_interest_scoring")
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# 定义颜色
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SOFT_BLUE = "\033[38;5;67m"
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RESET_COLOR = "\033[0m"
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class ChatterInterestScoringSystem:
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"""兴趣度评分系统"""
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def __init__(self):
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# 智能兴趣匹配配置
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self.use_smart_matching = True
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# 从配置加载评分权重
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affinity_config = global_config.affinity_flow
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self.score_weights = {
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"interest_match": affinity_config.keyword_match_weight, # 兴趣匹配度权重
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"relationship": affinity_config.relationship_weight, # 关系分权重
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"mentioned": affinity_config.mention_bot_weight, # 是否提及bot权重
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}
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# 评分阈值
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self.reply_threshold = affinity_config.reply_action_interest_threshold # 回复动作兴趣阈值
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self.mention_threshold = affinity_config.mention_bot_adjustment_threshold # 提及bot后的调整阈值
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# 连续不回复概率提升
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self.no_reply_count = 0
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self.max_no_reply_count = affinity_config.max_no_reply_count
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self.probability_boost_per_no_reply = (
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affinity_config.no_reply_threshold_adjustment / affinity_config.max_no_reply_count
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) # 每次不回复增加的概率
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# 用户关系数据
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self.user_relationships: dict[str, float] = {} # user_id -> relationship_score
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async def calculate_interest_scores(
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self, messages: list[DatabaseMessages], bot_nickname: str
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) -> list[InterestScore]:
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"""计算消息的兴趣度评分"""
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user_messages = [msg for msg in messages if str(msg.user_info.user_id) != str(global_config.bot.qq_account)]
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if not user_messages:
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return []
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scores = []
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for _, msg in enumerate(user_messages, 1):
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score = await self._calculate_single_message_score(msg, bot_nickname)
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scores.append(score)
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return scores
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async def _calculate_single_message_score(self, message: DatabaseMessages, bot_nickname: str) -> InterestScore:
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"""计算单条消息的兴趣度评分"""
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keywords = self._extract_keywords_from_database(message)
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interest_match_score = await self._calculate_interest_match_score(message.processed_plain_text, keywords)
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relationship_score = await self._calculate_relationship_score(message.user_info.user_id)
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mentioned_score = self._calculate_mentioned_score(message, bot_nickname)
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total_score = (
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interest_match_score * self.score_weights["interest_match"]
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+ relationship_score * self.score_weights["relationship"]
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+ mentioned_score * self.score_weights["mentioned"]
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)
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details = {
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"interest_match": f"兴趣匹配: {interest_match_score:.3f}",
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"relationship": f"关系: {relationship_score:.3f}",
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"mentioned": f"提及: {mentioned_score:.3f}",
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}
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logger.debug(
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f"消息得分详情: {total_score:.3f} (匹配: {interest_match_score:.2f}, 关系: {relationship_score:.2f}, 提及: {mentioned_score:.2f})"
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)
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return InterestScore(
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message_id=message.message_id,
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total_score=total_score,
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interest_match_score=interest_match_score,
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relationship_score=relationship_score,
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mentioned_score=mentioned_score,
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details=details,
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)
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async def _calculate_interest_match_score(self, content: str, keywords: list[str] | None = None) -> float:
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"""计算兴趣匹配度 - 使用智能embedding匹配"""
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if not content:
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return 0.0
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# 使用智能匹配（embedding）
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if self.use_smart_matching and bot_interest_manager.is_initialized:
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return await self._calculate_smart_interest_match(content, keywords)
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else:
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# 智能匹配未初始化，返回默认分数
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return 0.3
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async def _calculate_smart_interest_match(self, content: str, keywords: list[str] | None = None) -> float:
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"""使用embedding计算智能兴趣匹配"""
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try:
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# 如果没有传入关键词，则提取
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if not keywords:
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keywords = self._extract_keywords_from_content(content)
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# 使用机器人兴趣管理器计算匹配度
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match_result = await bot_interest_manager.calculate_interest_match(content, keywords)
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if match_result:
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# 返回匹配分数，考虑置信度和匹配标签数量
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affinity_config = global_config.affinity_flow
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match_count_bonus = min(
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len(match_result.matched_tags) * affinity_config.match_count_bonus, affinity_config.max_match_bonus
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)
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final_score = match_result.overall_score * 1.15 * match_result.confidence + match_count_bonus
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return final_score
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else:
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return 0.0
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except Exception as e:
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logger.error(f"智能兴趣匹配计算失败: {e}")
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return 0.0
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def _extract_keywords_from_database(self, message: DatabaseMessages) -> list[str]:
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"""从数据库消息中提取关键词"""
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keywords = []
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# 尝试从 key_words 字段提取（存储的是JSON字符串）
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if message.key_words:
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try:
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import orjson
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keywords = orjson.loads(message.key_words)
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if not isinstance(keywords, list):
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keywords = []
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except (orjson.JSONDecodeError, TypeError):
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keywords = []
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# 如果没有 keywords，尝试从 key_words_lite 提取
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if not keywords and message.key_words_lite:
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try:
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import orjson
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keywords = orjson.loads(message.key_words_lite)
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if not isinstance(keywords, list):
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keywords = []
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except (orjson.JSONDecodeError, TypeError):
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keywords = []
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# 如果还是没有，从消息内容中提取（降级方案）
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if not keywords:
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keywords = self._extract_keywords_from_content(message.processed_plain_text)
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return keywords[:15] # 返回前15个关键词
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def _extract_keywords_from_content(self, content: str) -> list[str]:
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"""从内容中提取关键词（降级方案）"""
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import re
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# 清理文本
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content = re.sub(r"[^\w\s\u4e00-\u9fff]", " ", content) # 保留中文、英文、数字
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words = content.split()
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# 过滤和关键词提取
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keywords = []
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for word in words:
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word = word.strip()
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if (
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len(word) >= 2 # 至少2个字符
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and word.isalnum() # 字母数字
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and not word.isdigit()
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): # 不是纯数字
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keywords.append(word.lower())
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# 去重并限制数量
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unique_keywords = list(set(keywords))
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return unique_keywords[:10] # 返回前10个唯一关键词
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async def _calculate_relationship_score(self, user_id: str) -> float:
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"""计算关系分 - 从数据库获取关系分"""
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# 优先使用内存中的关系分
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if user_id in self.user_relationships:
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relationship_value = self.user_relationships[user_id]
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return min(relationship_value, 1.0)
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# 如果内存中没有，尝试从关系追踪器获取
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if hasattr(self, "relationship_tracker") and self.relationship_tracker:
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try:
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relationship_score = await self.relationship_tracker.get_user_relationship_score(user_id)
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# 同时更新内存缓存
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self.user_relationships[user_id] = relationship_score
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return relationship_score
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except Exception:
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pass
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else:
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# 尝试从全局关系追踪器获取
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try:
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from .relationship_tracker import ChatterRelationshipTracker
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global_tracker = ChatterRelationshipTracker()
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if global_tracker:
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relationship_score = await global_tracker.get_user_relationship_score(user_id)
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# 同时更新内存缓存
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self.user_relationships[user_id] = relationship_score
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return relationship_score
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except Exception:
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pass
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# 默认新用户的基础分
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return global_config.affinity_flow.base_relationship_score
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def _calculate_mentioned_score(self, msg: DatabaseMessages, bot_nickname: str) -> float:
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"""计算提及分数"""
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if not msg.processed_plain_text:
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return 0.0
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# 检查是否被提及
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bot_aliases = [bot_nickname, *global_config.bot.alias_names]
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is_mentioned = msg.is_mentioned or any(alias in msg.processed_plain_text for alias in bot_aliases if alias)
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# 如果被提及或是私聊，都视为提及了bot
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if is_mentioned or not hasattr(msg, "chat_info_group_id"):
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return global_config.affinity_flow.mention_bot_interest_score
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return 0.0
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def should_reply(self, score: InterestScore, message: "DatabaseMessages") -> bool:
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"""判断是否应该回复"""
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base_threshold = self.reply_threshold
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# 如果被提及，降低阈值
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if score.mentioned_score >= global_config.affinity_flow.mention_bot_adjustment_threshold:
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base_threshold = self.mention_threshold
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# 计算连续不回复的概率提升
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probability_boost = min(self.no_reply_count * self.probability_boost_per_no_reply, 0.8)
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effective_threshold = base_threshold - probability_boost
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# 做出决策
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should_reply = score.total_score >= effective_threshold
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decision = "回复" if should_reply else "不回复"
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logger.info(
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f"{SOFT_BLUE}决策: {decision} (兴趣度: {score.total_score:.3f} / 阈值: {effective_threshold:.3f}){RESET_COLOR}"
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)
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return should_reply, score.total_score
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def record_reply_action(self, did_reply: bool):
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"""记录回复动作"""
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old_count = self.no_reply_count
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if did_reply:
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self.no_reply_count = max(0, self.no_reply_count - global_config.affinity_flow.reply_cooldown_reduction)
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action = "回复"
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else:
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self.no_reply_count += 1
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action = "不回复"
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# 限制最大计数
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self.no_reply_count = min(self.no_reply_count, self.max_no_reply_count)
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logger.info(f"动作: {action}, 连续不回复次数: {old_count} -> {self.no_reply_count}")
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def update_user_relationship(self, user_id: str, relationship_change: float):
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"""更新用户关系"""
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old_score = self.user_relationships.get(
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user_id, global_config.affinity_flow.base_relationship_score
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) # 默认新用户分数
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new_score = max(0.0, min(1.0, old_score + relationship_change))
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self.user_relationships[user_id] = new_score
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logger.info(f"用户关系: {user_id} | {old_score:.3f} → {new_score:.3f}")
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def get_user_relationship(self, user_id: str) -> float:
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"""获取用户关系分"""
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return self.user_relationships.get(user_id, 0.3)
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def get_scoring_stats(self) -> dict:
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"""获取评分系统统计"""
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return {
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"no_reply_count": self.no_reply_count,
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"max_no_reply_count": self.max_no_reply_count,
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"reply_threshold": self.reply_threshold,
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"mention_threshold": self.mention_threshold,
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"user_relationships": len(self.user_relationships),
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}
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def reset_stats(self):
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"""重置统计信息"""
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self.no_reply_count = 0
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logger.info("重置兴趣度评分系统统计")
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async def initialize_smart_interests(self, personality_description: str, personality_id: str = "default"):
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"""初始化智能兴趣系统"""
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try:
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logger.info("开始初始化智能兴趣系统...")
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logger.info(f"人设ID: {personality_id}, 描述长度: {len(personality_description)}")
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await bot_interest_manager.initialize(personality_description, personality_id)
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logger.info("智能兴趣系统初始化完成。")
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# 显示初始化后的统计信息
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bot_interest_manager.get_interest_stats()
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except Exception as e:
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logger.error(f"初始化智能兴趣系统失败: {e}")
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traceback.print_exc()
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def get_matching_config(self) -> dict[str, Any]:
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"""获取匹配配置信息"""
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return {
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"use_smart_matching": self.use_smart_matching,
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"smart_system_initialized": bot_interest_manager.is_initialized,
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"smart_system_stats": bot_interest_manager.get_interest_stats()
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if bot_interest_manager.is_initialized
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else None,
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}
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# 创建全局兴趣评分系统实例
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chatter_interest_scoring_system = ChatterInterestScoringSystem()
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@@ -27,7 +27,9 @@ class ChatterRelationshipTracker:
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self.update_interval_minutes = 30
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self.last_update_time = time.time()
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self.relationship_history: list[dict] = []
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self.interest_scoring_system = interest_scoring_system
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# 兼容性：保留参数但不直接使用，转而使用统一API
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self.interest_scoring_system = None # 废弃，不再使用
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# 用户关系缓存 (user_id -> {"relationship_text": str, "relationship_score": float, "last_tracked": float})
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self.user_relationship_cache: dict[str, dict] = {}
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@@ -63,8 +65,9 @@ class ChatterRelationshipTracker:
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)
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def set_interest_scoring_system(self, interest_scoring_system):
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"""设置兴趣度评分系统引用"""
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self.interest_scoring_system = interest_scoring_system
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"""设置兴趣度评分系统引用（已废弃，使用统一API）"""
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# 不再需要设置，直接使用统一API
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logger.info("set_interest_scoring_system 已废弃，现在使用统一评分API")
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def add_interaction(self, user_id: str, user_name: str, user_message: str, bot_reply: str, reply_timestamp: float):
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"""添加用户交互记录"""
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@@ -75,10 +78,10 @@ class ChatterRelationshipTracker:
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)
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del self.tracking_users[oldest_user]
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# 获取当前关系分
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# 获取当前关系分 - 使用缓存数据
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current_relationship_score = global_config.affinity_flow.base_relationship_score # 默认值
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if self.interest_scoring_system:
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current_relationship_score = self.interest_scoring_system.get_user_relationship(user_id)
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if user_id in self.user_relationship_cache:
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current_relationship_score = self.user_relationship_cache[user_id].get("relationship_score", current_relationship_score)
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self.tracking_users[user_id] = {
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"user_id": user_id,
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@@ -178,10 +181,11 @@ class ChatterRelationshipTracker:
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),
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)
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if self.interest_scoring_system:
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self.interest_scoring_system.update_user_relationship(
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interaction["user_id"], new_score - interaction["current_relationship_score"]
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)
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# 使用统一API更新关系分
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from src.plugin_system.apis.scoring_api import scoring_api
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await scoring_api.update_user_relationship(
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interaction["user_id"], new_score
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)
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return {
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"user_id": interaction["user_id"],
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@@ -252,12 +256,14 @@ class ChatterRelationshipTracker:
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self.update_interval_minutes = update_interval_minutes
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logger.info(f"更新关系更新间隔: {update_interval_minutes} 分钟")
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def force_update_relationship(self, user_id: str, new_score: float, reasoning: str = ""):
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async def force_update_relationship(self, user_id: str, new_score: float, reasoning: str = ""):
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"""强制更新用户关系分"""
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if user_id in self.tracking_users:
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current_score = self.tracking_users[user_id]["current_relationship_score"]
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if self.interest_scoring_system:
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self.interest_scoring_system.update_user_relationship(user_id, new_score - current_score)
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# 使用统一API更新关系分
|
||||
from src.plugin_system.apis.scoring_api import scoring_api
|
||||
await scoring_api.update_user_relationship(user_id, new_score)
|
||||
|
||||
update_info = {
|
||||
"user_id": user_id,
|
||||
@@ -605,9 +611,8 @@ class ChatterRelationshipTracker:
|
||||
"last_tracked": time.time(),
|
||||
}
|
||||
|
||||
# 如果有兴趣度评分系统，也更新内存中的关系分
|
||||
if self.interest_scoring_system:
|
||||
self.interest_scoring_system.update_user_relationship(user_id, new_score - current_score)
|
||||
# 使用统一API更新关系分（内存缓存已通过数据库更新自动处理）
|
||||
# 数据库更新后，缓存会在下次访问时自动同步
|
||||
|
||||
# 记录分析历史
|
||||
analysis_record = {
|
||||
|
||||
Reference in New Issue
Block a user