cfa642cf0a
- 改进表达学习器的提示词格式规范，增强LLM输出解析的容错性 - 优化表达选择器的模型预测模式，添加情境提取和模糊匹配机制 - 增强StyleLearner的错误处理和日志记录，提高训练和预测的稳定性 - 改进流循环管理器的日志输出，避免重复信息刷屏 - 扩展SendAPI的消息查找功能，支持DatabaseMessages对象兼容 - 添加智能回退机制，当模型预测失败时自动切换到经典模式 - 优化数据库查询逻辑，支持跨聊天流的表达方式共享 BREAKING CHANGE: 表达选择器的模型预测模式现在需要情境提取器配合使用，旧版本配置可能需要更新依赖关系
426 lines
13 KiB
Python
426 lines
13 KiB
Python
"""
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风格学习引擎
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基于ExpressorModel实现的表达风格学习和预测系统
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支持多聊天室独立建模和在线学习
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"""
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import os
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import time
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from typing import Dict, List, Optional, Tuple
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from src.common.logger import get_logger
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from .expressor_model import ExpressorModel
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logger = get_logger("expressor.style_learner")
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class StyleLearner:
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"""单个聊天室的表达风格学习器"""
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def __init__(self, chat_id: str, model_config: Optional[Dict] = None):
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"""
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Args:
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chat_id: 聊天室ID
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model_config: 模型配置
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"""
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self.chat_id = chat_id
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self.model_config = model_config or {
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"alpha": 0.5,
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"beta": 0.5,
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"gamma": 0.99, # 衰减因子，支持遗忘
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"vocab_size": 200000,
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"use_jieba": True,
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}
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# 初始化表达模型
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self.expressor = ExpressorModel(**self.model_config)
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# 动态风格管理
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self.max_styles = 2000 # 每个chat_id最多2000个风格
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self.style_to_id: Dict[str, str] = {} # style文本 -> style_id
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self.id_to_style: Dict[str, str] = {} # style_id -> style文本
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self.id_to_situation: Dict[str, str] = {} # style_id -> situation文本
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self.next_style_id = 0
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# 学习统计
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self.learning_stats = {
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"total_samples": 0,
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"style_counts": {},
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"last_update": time.time(),
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}
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logger.info(f"StyleLearner初始化成功: chat_id={chat_id}")
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def add_style(self, style: str, situation: Optional[str] = None) -> bool:
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"""
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动态添加一个新的风格
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Args:
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style: 风格文本
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situation: 情境文本
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Returns:
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是否添加成功
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"""
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try:
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# 检查是否已存在
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if style in self.style_to_id:
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return True
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# 检查是否超过最大限制
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if len(self.style_to_id) >= self.max_styles:
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logger.warning(f"已达到最大风格数量限制 ({self.max_styles})")
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return False
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# 生成新的style_id
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style_id = f"style_{self.next_style_id}"
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self.next_style_id += 1
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# 添加到映射
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self.style_to_id[style] = style_id
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self.id_to_style[style_id] = style
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if situation:
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self.id_to_situation[style_id] = situation
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# 添加到expressor模型
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self.expressor.add_candidate(style_id, style, situation)
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# 初始化统计
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self.learning_stats["style_counts"][style_id] = 0
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logger.debug(f"添加风格成功: {style_id} -> {style}")
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return True
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except Exception as e:
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logger.error(f"添加风格失败: {e}")
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return False
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def learn_mapping(self, up_content: str, style: str) -> bool:
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"""
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学习一个up_content到style的映射
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Args:
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up_content: 前置内容
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style: 目标风格
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Returns:
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是否学习成功
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"""
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try:
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# 如果style不存在，先添加它
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if style not in self.style_to_id:
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if not self.add_style(style):
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return False
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# 获取style_id
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style_id = self.style_to_id[style]
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# 使用正反馈学习
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self.expressor.update_positive(up_content, style_id)
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# 更新统计
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self.learning_stats["total_samples"] += 1
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self.learning_stats["style_counts"][style_id] += 1
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self.learning_stats["last_update"] = time.time()
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logger.debug(f"学习映射成功: {up_content[:20]}... -> {style}")
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return True
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except Exception as e:
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logger.error(f"学习映射失败: {e}")
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return False
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def predict_style(self, up_content: str, top_k: int = 5) -> Tuple[Optional[str], Dict[str, float]]:
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"""
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根据up_content预测最合适的style
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Args:
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up_content: 前置内容
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top_k: 返回前k个候选
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Returns:
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(最佳style文本, 所有候选的分数字典)
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"""
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try:
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# 先检查是否有训练数据
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if not self.style_to_id:
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logger.debug(f"StyleLearner还没有任何训练数据: chat_id={self.chat_id}")
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return None, {}
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best_style_id, scores = self.expressor.predict(up_content, k=top_k)
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if best_style_id is None:
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logger.debug(f"ExpressorModel未返回预测结果: chat_id={self.chat_id}, up_content={up_content[:50]}...")
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return None, {}
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# 将style_id转换为style文本
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best_style = self.id_to_style.get(best_style_id)
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if best_style is None:
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logger.warning(
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f"style_id无法转换为style文本: style_id={best_style_id}, "
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f"已知的id_to_style数量={len(self.id_to_style)}"
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)
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return None, {}
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# 转换所有分数
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style_scores = {}
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for sid, score in scores.items():
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style_text = self.id_to_style.get(sid)
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if style_text:
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style_scores[style_text] = score
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else:
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logger.warning(f"跳过无法转换的style_id: {sid}")
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logger.debug(
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f"预测成功: up_content={up_content[:30]}..., "
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f"best_style={best_style}, top3_scores={list(style_scores.items())[:3]}"
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)
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return best_style, style_scores
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except Exception as e:
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logger.error(f"预测style失败: {e}", exc_info=True)
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return None, {}
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def get_style_info(self, style: str) -> Tuple[Optional[str], Optional[str]]:
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"""
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获取style的完整信息
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Args:
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style: 风格文本
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Returns:
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(style_id, situation)
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"""
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style_id = self.style_to_id.get(style)
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if not style_id:
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return None, None
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situation = self.id_to_situation.get(style_id)
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return style_id, situation
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def get_all_styles(self) -> List[str]:
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"""
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获取所有风格列表
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Returns:
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风格文本列表
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"""
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return list(self.style_to_id.keys())
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def apply_decay(self, factor: Optional[float] = None):
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"""
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应用知识衰减
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Args:
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factor: 衰减因子
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"""
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self.expressor.decay(factor)
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logger.debug(f"应用知识衰减: chat_id={self.chat_id}")
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def save(self, base_path: str) -> bool:
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"""
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保存学习器到文件
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Args:
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base_path: 基础保存路径
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Returns:
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是否保存成功
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"""
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try:
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# 创建保存目录
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save_dir = os.path.join(base_path, self.chat_id)
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os.makedirs(save_dir, exist_ok=True)
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# 保存expressor模型
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model_path = os.path.join(save_dir, "expressor_model.pkl")
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self.expressor.save(model_path)
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# 保存映射关系和统计信息
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import pickle
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meta_path = os.path.join(save_dir, "meta.pkl")
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meta_data = {
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"style_to_id": self.style_to_id,
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"id_to_style": self.id_to_style,
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"id_to_situation": self.id_to_situation,
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"next_style_id": self.next_style_id,
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"learning_stats": self.learning_stats,
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}
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with open(meta_path, "wb") as f:
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pickle.dump(meta_data, f)
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logger.info(f"StyleLearner保存成功: {save_dir}")
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return True
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except Exception as e:
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logger.error(f"保存StyleLearner失败: {e}")
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return False
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def load(self, base_path: str) -> bool:
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"""
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从文件加载学习器
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Args:
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base_path: 基础加载路径
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Returns:
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是否加载成功
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"""
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try:
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save_dir = os.path.join(base_path, self.chat_id)
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# 检查目录是否存在
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if not os.path.exists(save_dir):
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logger.debug(f"StyleLearner保存目录不存在: {save_dir}")
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return False
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# 加载expressor模型
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model_path = os.path.join(save_dir, "expressor_model.pkl")
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if os.path.exists(model_path):
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self.expressor.load(model_path)
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# 加载映射关系和统计信息
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import pickle
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meta_path = os.path.join(save_dir, "meta.pkl")
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if os.path.exists(meta_path):
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with open(meta_path, "rb") as f:
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meta_data = pickle.load(f)
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self.style_to_id = meta_data["style_to_id"]
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self.id_to_style = meta_data["id_to_style"]
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self.id_to_situation = meta_data["id_to_situation"]
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self.next_style_id = meta_data["next_style_id"]
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self.learning_stats = meta_data["learning_stats"]
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logger.info(f"StyleLearner加载成功: {save_dir}")
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return True
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except Exception as e:
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logger.error(f"加载StyleLearner失败: {e}")
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return False
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def get_stats(self) -> Dict:
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"""获取统计信息"""
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model_stats = self.expressor.get_stats()
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return {
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"chat_id": self.chat_id,
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"n_styles": len(self.style_to_id),
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"total_samples": self.learning_stats["total_samples"],
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"last_update": self.learning_stats["last_update"],
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"model_stats": model_stats,
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}
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class StyleLearnerManager:
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"""多聊天室表达风格学习管理器"""
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def __init__(self, model_save_path: str = "data/expression/style_models"):
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"""
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Args:
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model_save_path: 模型保存路径
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"""
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self.learners: Dict[str, StyleLearner] = {}
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self.model_save_path = model_save_path
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# 确保保存目录存在
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os.makedirs(model_save_path, exist_ok=True)
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logger.info(f"StyleLearnerManager初始化成功, 模型保存路径: {model_save_path}")
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def get_learner(self, chat_id: str, model_config: Optional[Dict] = None) -> StyleLearner:
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"""
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获取或创建指定chat_id的学习器
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Args:
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chat_id: 聊天室ID
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model_config: 模型配置
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Returns:
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StyleLearner实例
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"""
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if chat_id not in self.learners:
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# 创建新的学习器
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learner = StyleLearner(chat_id, model_config)
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# 尝试加载已保存的模型
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learner.load(self.model_save_path)
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self.learners[chat_id] = learner
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return self.learners[chat_id]
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def learn_mapping(self, chat_id: str, up_content: str, style: str) -> bool:
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"""
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学习一个映射关系
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Args:
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chat_id: 聊天室ID
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up_content: 前置内容
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style: 目标风格
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Returns:
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是否学习成功
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"""
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learner = self.get_learner(chat_id)
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return learner.learn_mapping(up_content, style)
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def predict_style(self, chat_id: str, up_content: str, top_k: int = 5) -> Tuple[Optional[str], Dict[str, float]]:
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"""
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预测最合适的风格
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Args:
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chat_id: 聊天室ID
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up_content: 前置内容
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top_k: 返回前k个候选
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Returns:
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(最佳style, 分数字典)
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"""
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learner = self.get_learner(chat_id)
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return learner.predict_style(up_content, top_k)
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def save_all(self) -> bool:
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"""
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保存所有学习器
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Returns:
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是否全部保存成功
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"""
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success = True
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for chat_id, learner in self.learners.items():
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if not learner.save(self.model_save_path):
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success = False
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logger.info(f"保存所有StyleLearner {'成功' if success else '部分失败'}")
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return success
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def apply_decay_all(self, factor: Optional[float] = None):
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"""
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对所有学习器应用知识衰减
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Args:
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factor: 衰减因子
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"""
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for learner in self.learners.values():
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learner.apply_decay(factor)
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logger.info(f"对所有StyleLearner应用知识衰减")
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def get_all_stats(self) -> Dict[str, Dict]:
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"""
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获取所有学习器的统计信息
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Returns:
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{chat_id: stats}
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"""
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return {chat_id: learner.get_stats() for chat_id, learner in self.learners.items()}
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# 全局单例
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style_learner_manager = StyleLearnerManager()
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