import os import random import time from datetime import datetime from typing import Any import orjson from sqlalchemy import select from src.chat.message_receive.chat_stream import get_chat_manager from src.chat.utils.chat_message_builder import build_anonymous_messages, get_raw_msg_by_timestamp_with_chat_inclusive from src.chat.utils.prompt import Prompt, global_prompt_manager from src.common.database.sqlalchemy_database_api import get_db_session from src.common.database.sqlalchemy_models import Expression from src.common.logger import get_logger from src.config.config import global_config, model_config from src.llm_models.utils_model import LLMRequest # 导入 StyleLearner 管理器 from .style_learner import style_learner_manager MAX_EXPRESSION_COUNT = 300 DECAY_DAYS = 30 # 30天衰减到0.01 DECAY_MIN = 0.01 # 最小衰减值 logger = get_logger("expressor") def format_create_date(timestamp: float) -> str: """ 将时间戳格式化为可读的日期字符串 """ try: return datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S") except (ValueError, OSError): return "未知时间" def init_prompt() -> None: learn_style_prompt = """ {chat_str} 请从上面这段群聊中概括除了人名为"SELF"之外的人的语言风格 1. 只考虑文字，不要考虑表情包和图片 2. 不要涉及具体的人名，只考虑语言风格 3. 语言风格包含特殊内容和情感 4. 思考有没有特殊的梗，一并总结成语言风格 5. 例子仅供参考，请严格根据群聊内容总结!!! 注意：总结成如下格式的规律，总结的内容要详细，但具有概括性： 例如：当"AAAAA"时，可以"BBBBB", AAAAA代表某个具体的场景，不超过20个字。BBBBB代表对应的语言风格，特定句式或表达方式，不超过20个字。 例如： 当"对某件事表示十分惊叹，有些意外"时，使用"我嘞个xxxx" 当"表示讽刺的赞同，不想讲道理"时，使用"对对对" 当"想说明某个具体的事实观点，但懒得明说，或者不便明说，或表达一种默契"，使用"懂的都懂" 当"当涉及游戏相关时，表示意外的夸赞，略带戏谑意味"时，使用"这么强！" 请注意：不要总结你自己（SELF）的发言 现在请你概括 """ Prompt(learn_style_prompt, "learn_style_prompt") learn_grammar_prompt = """ {chat_str} 请从上面这段群聊中概括除了人名为"SELF"之外的人的语法和句法特点，只考虑纯文字，不要考虑表情包和图片 1.不要总结【图片】，【动画表情】，[图片]，[动画表情]，不总结 表情符号 at @ 回复 和[回复] 2.不要涉及具体的人名，只考虑语法和句法特点, 3.语法和句法特点要包括，句子长短（具体字数），有何种语病，如何拆分句子。 4. 例子仅供参考，请严格根据群聊内容总结!!! 总结成如下格式的规律，总结的内容要简洁，不浮夸： 当"xxx"时，可以"xxx" 例如： 当"表达观点较复杂"时，使用"省略主语(3-6个字)"的句法 当"不用详细说明的一般表达"时，使用"非常简洁的句子"的句法 当"需要单纯简单的确认"时，使用"单字或几个字的肯定(1-2个字)"的句法 注意不要总结你自己（SELF）的发言 现在请你概括 """ Prompt(learn_grammar_prompt, "learn_grammar_prompt") class ExpressionLearner: def __init__(self, chat_id: str) -> None: self.express_learn_model: LLMRequest = LLMRequest( model_set=model_config.model_task_config.replyer, request_type="expressor.learner" ) self.chat_id = chat_id self.chat_name = chat_id # 初始化时使用chat_id，稍后异步更新 # 维护每个chat的上次学习时间 self.last_learning_time: float = time.time() # 学习参数 self.min_messages_for_learning = 25 # 触发学习所需的最少消息数 self.min_learning_interval = 300 # 最短学习时间间隔（秒） self._chat_name_initialized = False async def _initialize_chat_name(self): """异步初始化chat_name""" if not self._chat_name_initialized: stream_name = await get_chat_manager().get_stream_name(self.chat_id) self.chat_name = stream_name or self.chat_id self._chat_name_initialized = True def can_learn_for_chat(self) -> bool: """ 检查指定聊天流是否允许学习表达 Args: chat_id: 聊天流ID Returns: bool: 是否允许学习 """ try: use_expression, enable_learning, _ = global_config.expression.get_expression_config_for_chat(self.chat_id) return enable_learning except Exception as e: logger.error(f"检查学习权限失败: {e}") return False async def should_trigger_learning(self) -> bool: """ 检查是否应该触发学习 Args: chat_id: 聊天流ID Returns: bool: 是否应该触发学习 """ current_time = time.time() # 获取该聊天流的学习强度 try: use_expression, enable_learning, learning_intensity = ( global_config.expression.get_expression_config_for_chat(self.chat_id) ) except Exception as e: logger.error(f"获取聊天流 {self.chat_id} 的学习配置失败: {e}") return False # 检查是否允许学习 if not enable_learning: return False # 根据学习强度计算最短学习时间间隔 min_interval = self.min_learning_interval / learning_intensity # 检查时间间隔 time_diff = current_time - self.last_learning_time if time_diff < min_interval: return False # 检查消息数量（只检查指定聊天流的消息） recent_messages = await get_raw_msg_by_timestamp_with_chat_inclusive( chat_id=self.chat_id, timestamp_start=self.last_learning_time, timestamp_end=time.time(), ) if not recent_messages or len(recent_messages) < self.min_messages_for_learning: return False return True async def trigger_learning_for_chat(self) -> bool: """ 为指定聊天流触发学习 Args: chat_id: 聊天流ID Returns: bool: 是否成功触发学习 """ # 初始化chat_name await self._initialize_chat_name() if not await self.should_trigger_learning(): return False try: logger.info(f"为聊天流 {self.chat_name} 触发表达学习") # 学习语言风格 learnt_style = await self.learn_and_store(type="style", num=25) # 学习句法特点 learnt_grammar = await self.learn_and_store(type="grammar", num=10) # 更新学习时间 self.last_learning_time = time.time() if learnt_style or learnt_grammar: logger.info(f"聊天流 {self.chat_name} 表达学习完成") return True else: logger.warning(f"聊天流 {self.chat_name} 表达学习未获得有效结果") return False except Exception as e: logger.error(f"为聊天流 {self.chat_name} 触发学习失败: {e}") return False async def get_expression_by_chat_id(self) -> tuple[list[dict[str, float]], list[dict[str, float]]]: """ 获取指定chat_id的style和grammar表达方式 返回的每个表达方式字典中都包含了source_id, 用于后续的更新操作 """ learnt_style_expressions = [] learnt_grammar_expressions = [] # 直接从数据库查询 async with get_db_session() as session: style_query = await session.execute( select(Expression).where((Expression.chat_id == self.chat_id) & (Expression.type == "style")) ) for expr in style_query.scalars(): # 确保create_date存在，如果不存在则使用last_active_time create_date = expr.create_date if expr.create_date is not None else expr.last_active_time learnt_style_expressions.append( { "situation": expr.situation, "style": expr.style, "count": expr.count, "last_active_time": expr.last_active_time, "source_id": self.chat_id, "type": "style", "create_date": create_date, } ) grammar_query = await session.execute( select(Expression).where((Expression.chat_id == self.chat_id) & (Expression.type == "grammar")) ) for expr in grammar_query.scalars(): # 确保create_date存在，如果不存在则使用last_active_time create_date = expr.create_date if expr.create_date is not None else expr.last_active_time learnt_grammar_expressions.append( { "situation": expr.situation, "style": expr.style, "count": expr.count, "last_active_time": expr.last_active_time, "source_id": self.chat_id, "type": "grammar", "create_date": create_date, } ) return learnt_style_expressions, learnt_grammar_expressions async def _apply_global_decay_to_database(self, current_time: float) -> None: """ 对数据库中的所有表达方式应用全局衰减 """ try: async with get_db_session() as session: # 获取所有表达方式 all_expressions = await session.execute(select(Expression)) all_expressions = all_expressions.scalars().all() updated_count = 0 deleted_count = 0 for expr in all_expressions: # 计算时间差 last_active = expr.last_active_time time_diff_days = (current_time - last_active) / (24 * 3600) # 转换为天 # 计算衰减值 decay_value = self.calculate_decay_factor(time_diff_days) new_count = max(0.01, expr.count - decay_value) if new_count <= 0.01: # 如果count太小，删除这个表达方式 await session.delete(expr) await session.commit() deleted_count += 1 else: # 更新count expr.count = new_count updated_count += 1 if updated_count > 0 or deleted_count > 0: logger.info(f"全局衰减完成：更新了 {updated_count} 个表达方式，删除了 {deleted_count} 个表达方式") except Exception as e: logger.error(f"数据库全局衰减失败: {e}") @staticmethod def calculate_decay_factor(time_diff_days: float) -> float: """ 计算衰减值 当时间差为0天时，衰减值为0（最近活跃的不衰减） 当时间差为7天时，衰减值为0.002（中等衰减） 当时间差为30天或更长时，衰减值为0.01（高衰减） 使用二次函数进行曲线插值 """ if time_diff_days <= 0: return 0.0 # 刚激活的表达式不衰减 if time_diff_days >= DECAY_DAYS: return 0.01 # 长时间未活跃的表达式大幅衰减 # 使用二次函数插值：在0-30天之间从0衰减到0.01 # 使用简单的二次函数：y = a * x^2 # 当x=30时，y=0.01，所以 a = 0.01 / (30^2) = 0.01 / 900 a = 0.01 / (DECAY_DAYS**2) decay = a * (time_diff_days**2) return min(0.01, decay) async def learn_and_store(self, type: str, num: int = 10) -> None | list[Any] | list[tuple[str, str, str]]: # sourcery skip: use-join """ 学习并存储表达方式 type: "style" or "grammar" """ if type == "style": type_str = "语言风格" elif type == "grammar": type_str = "句法特点" else: raise ValueError(f"Invalid type: {type}") # 检查是否允许在此聊天流中学习（在函数最前面检查） if not self.can_learn_for_chat(): logger.debug(f"聊天流 {self.chat_name} 不允许学习表达，跳过学习") return [] res = await self.learn_expression(type, num) if res is None: return [] learnt_expressions, chat_id = res chat_stream = await get_chat_manager().get_stream(chat_id) if chat_stream is None: group_name = f"聊天流 {chat_id}" elif chat_stream.group_info: group_name = chat_stream.group_info.group_name else: group_name = f"{chat_stream.user_info.user_nickname}的私聊" learnt_expressions_str = "" for _chat_id, situation, style in learnt_expressions: learnt_expressions_str += f"{situation}->{style}\n" logger.info(f"在 {group_name} 学习到{type_str}:\n{learnt_expressions_str}") if not learnt_expressions: logger.info(f"没有学习到{type_str}") return [] # 按chat_id分组 chat_dict: dict[str, list[dict[str, Any]]] = {} for chat_id, situation, style in learnt_expressions: if chat_id not in chat_dict: chat_dict[chat_id] = [] chat_dict[chat_id].append({"situation": situation, "style": style}) current_time = time.time() # 存储到数据库 Expression 表 for chat_id, expr_list in chat_dict.items(): async with get_db_session() as session: for new_expr in expr_list: # 查找是否已存在相似表达方式 query = await session.execute( select(Expression).where( (Expression.chat_id == chat_id) & (Expression.type == type) & (Expression.situation == new_expr["situation"]) & (Expression.style == new_expr["style"]) ) ) existing_expr = query.scalar() if existing_expr: expr_obj = existing_expr # 50%概率替换内容 if random.random() < 0.5: expr_obj.situation = new_expr["situation"] expr_obj.style = new_expr["style"] expr_obj.count = expr_obj.count + 1 expr_obj.last_active_time = current_time else: new_expression = Expression( situation=new_expr["situation"], style=new_expr["style"], count=1, last_active_time=current_time, chat_id=chat_id, type=type, create_date=current_time, # 手动设置创建日期 ) session.add(new_expression) # 限制最大数量 exprs_result = await session.execute( select(Expression) .where((Expression.chat_id == chat_id) & (Expression.type == type)) .order_by(Expression.count.asc()) ) exprs = list(exprs_result.scalars()) if len(exprs) > MAX_EXPRESSION_COUNT: # 删除count最小的多余表达方式 for expr in exprs[: len(exprs) - MAX_EXPRESSION_COUNT]: await session.delete(expr) # 🔥 新增：训练 StyleLearner # 只对 style 类型的表达方式进行训练（grammar 不需要训练到模型） if type == "style": try: # 获取 StyleLearner 实例 learner = style_learner_manager.get_learner(chat_id) # 为每个学习到的表达方式训练模型 # 这里使用 situation 作为前置内容（context），style 作为目标风格 for expr in expr_list: situation = expr["situation"] style = expr["style"] # 训练映射关系: situation -> style learner.learn_mapping(situation, style) logger.debug(f"已将 {len(expr_list)} 个表达方式训练到 StyleLearner") # 保存模型 learner.save(style_learner_manager.model_save_path) except Exception as e: logger.error(f"训练 StyleLearner 失败: {e}") return learnt_expressions return None async def learn_expression(self, type: str, num: int = 10) -> tuple[list[tuple[str, str, str]], str] | None: """从指定聊天流学习表达方式 Args: type: "style" or "grammar" """ if type == "style": type_str = "语言风格" prompt = "learn_style_prompt" elif type == "grammar": type_str = "句法特点" prompt = "learn_grammar_prompt" else: raise ValueError(f"Invalid type: {type}") current_time = time.time() # 获取上次学习时间 random_msg: list[dict[str, Any]] | None = await get_raw_msg_by_timestamp_with_chat_inclusive( chat_id=self.chat_id, timestamp_start=self.last_learning_time, timestamp_end=current_time, limit=num, ) # print(random_msg) if not random_msg or random_msg == []: return None # 转化成str chat_id: str = random_msg[0]["chat_id"] # random_msg_str: str = build_readable_messages(random_msg, timestamp_mode="normal") random_msg_str: str = await build_anonymous_messages(random_msg) # print(f"random_msg_str:{random_msg_str}") prompt: str = await global_prompt_manager.format_prompt( prompt, chat_str=random_msg_str, ) logger.debug(f"学习{type_str}的prompt: {prompt}") try: response, _ = await self.express_learn_model.generate_response_async(prompt, temperature=0.3) except Exception as e: logger.error(f"学习{type_str}失败: {e}") return None logger.debug(f"学习{type_str}的response: {response}") expressions: list[tuple[str, str, str]] = self.parse_expression_response(response, chat_id) return expressions, chat_id @staticmethod def parse_expression_response(response: str, chat_id: str) -> list[tuple[str, str, str]]: """ 解析LLM返回的表达风格总结，每一行提取"当"和"使用"之间的内容，存储为(situation, style)元组 """ expressions: list[tuple[str, str, str]] = [] for line in response.splitlines(): line = line.strip() if not line: continue # 查找"当"和下一个引号 idx_when = line.find('当"') if idx_when == -1: continue idx_quote1 = idx_when + 1 idx_quote2 = line.find('"', idx_quote1 + 1) if idx_quote2 == -1: continue situation = line[idx_quote1 + 1 : idx_quote2] # 查找"使用" idx_use = line.find('使用"', idx_quote2) if idx_use == -1: continue idx_quote3 = idx_use + 2 idx_quote4 = line.find('"', idx_quote3 + 1) if idx_quote4 == -1: continue style = line[idx_quote3 + 1 : idx_quote4] expressions.append((chat_id, situation, style)) return expressions init_prompt() class ExpressionLearnerManager: def __init__(self): self.expression_learners = {} self._ensure_expression_directories() async def get_expression_learner(self, chat_id: str) -> ExpressionLearner: await self._auto_migrate_json_to_db() await self._migrate_old_data_create_date() if chat_id not in self.expression_learners: self.expression_learners[chat_id] = ExpressionLearner(chat_id) return self.expression_learners[chat_id] @staticmethod def _ensure_expression_directories(): """ 确保表达方式相关的目录结构存在 """ base_dir = os.path.join("data", "expression") directories_to_create = [ base_dir, os.path.join(base_dir, "learnt_style"), os.path.join(base_dir, "learnt_grammar"), ] for directory in directories_to_create: try: os.makedirs(directory, exist_ok=True) logger.debug(f"确保目录存在: {directory}") except Exception as e: logger.error(f"创建目录失败 {directory}: {e}") @staticmethod async def _auto_migrate_json_to_db(): """ 自动将/data/expression/learnt_style 和 learnt_grammar 下所有expressions.json迁移到数据库。 迁移完成后在/data/expression/done.done写入标记文件，存在则跳过。 """ base_dir = os.path.join("data", "expression") done_flag = os.path.join(base_dir, "done.done") # 确保基础目录存在 try: os.makedirs(base_dir, exist_ok=True) logger.debug(f"确保目录存在: {base_dir}") except Exception as e: logger.error(f"创建表达方式目录失败: {e}") return if os.path.exists(done_flag): logger.debug("表达方式JSON已迁移，无需重复迁移。") return logger.info("开始迁移表达方式JSON到数据库...") migrated_count = 0 for type in ["learnt_style", "learnt_grammar"]: type_str = "style" if type == "learnt_style" else "grammar" type_dir = os.path.join(base_dir, type) if not os.path.exists(type_dir): logger.debug(f"目录不存在，跳过: {type_dir}") continue try: chat_ids = os.listdir(type_dir) logger.debug(f"在 {type_dir} 中找到 {len(chat_ids)} 个聊天ID目录") except Exception as e: logger.error(f"读取目录失败 {type_dir}: {e}") continue for chat_id in chat_ids: expr_file = os.path.join(type_dir, chat_id, "expressions.json") if not os.path.exists(expr_file): continue try: with open(expr_file, encoding="utf-8") as f: expressions = orjson.loads(f.read()) if not isinstance(expressions, list): logger.warning(f"表达方式文件格式错误，跳过: {expr_file}") continue for expr in expressions: if not isinstance(expr, dict): continue situation = expr.get("situation") style_val = expr.get("style") count = expr.get("count", 1) last_active_time = expr.get("last_active_time", time.time()) if not situation or not style_val: logger.warning(f"表达方式缺少必要字段，跳过: {expr}") continue # 查重：同chat_id+type+situation+style async with get_db_session() as session: query = await session.execute( select(Expression).where( (Expression.chat_id == chat_id) & (Expression.type == type_str) & (Expression.situation == situation) & (Expression.style == style_val) ) ) existing_expr = query.scalar() if existing_expr: expr_obj = existing_expr expr_obj.count = max(expr_obj.count, count) expr_obj.last_active_time = max(expr_obj.last_active_time, last_active_time) else: new_expression = Expression( situation=situation, style=style_val, count=count, last_active_time=last_active_time, chat_id=chat_id, type=type_str, create_date=last_active_time, # 迁移时使用last_active_time作为创建时间 ) session.add(new_expression) migrated_count += 1 logger.info(f"已迁移 {expr_file} 到数据库，包含 {len(expressions)} 个表达方式") except orjson.JSONDecodeError as e: logger.error(f"JSON解析失败 {expr_file}: {e}") except Exception as e: logger.error(f"迁移表达方式 {expr_file} 失败: {e}") # 标记迁移完成 try: # 确保done.done文件的父目录存在 done_parent_dir = os.path.dirname(done_flag) if not os.path.exists(done_parent_dir): os.makedirs(done_parent_dir, exist_ok=True) logger.debug(f"为done.done创建父目录: {done_parent_dir}") with open(done_flag, "w", encoding="utf-8") as f: f.write("done\n") logger.info(f"表达方式JSON迁移已完成，共迁移 {migrated_count} 个表达方式，已写入done.done标记文件") except PermissionError as e: logger.error(f"权限不足，无法写入done.done标记文件: {e}") except OSError as e: logger.error(f"文件系统错误，无法写入done.done标记文件: {e}") except Exception as e: logger.error(f"写入done.done标记文件失败: {e}") @staticmethod async def _migrate_old_data_create_date(): """ 为没有create_date的老数据设置创建日期 使用last_active_time作为create_date的默认值 """ try: async with get_db_session() as session: # 查找所有create_date为空的表达方式 old_expressions_result = await session.execute( select(Expression).where(Expression.create_date.is_(None)) ) old_expressions = old_expressions_result.scalars().all() updated_count = 0 for expr in old_expressions: # 使用last_active_time作为create_date expr.create_date = expr.last_active_time updated_count += 1 if updated_count > 0: logger.info(f"已为 {updated_count} 个老的表达方式设置创建日期") except Exception as e: logger.error(f"迁移老数据创建日期失败: {e}") expression_learner_manager = ExpressionLearnerManager()