diff --git a/src/chat/chat_loop/heartFC_chat.py b/src/chat/chat_loop/heartFC_chat.py index e9a1dec12..2385c8399 100644 --- a/src/chat/chat_loop/heartFC_chat.py +++ b/src/chat/chat_loop/heartFC_chat.py @@ -233,9 +233,9 @@ class HeartFChatting: modified_exit_interest_threshold = 1.5 / talk_frequency total_interest = 0.0 for msg_dict in new_message: - interest_value = msg_dict.get("interest_value", 0.0) - if msg_dict.get("processed_plain_text", ""): - total_interest += interest_value + interest_value = msg_dict.get("interest_value") + if interest_value is not None and msg_dict.get("processed_plain_text", ""): + total_interest += float(interest_value) if new_message_count >= modified_exit_count_threshold: self.recent_interest_records.append(total_interest) @@ -244,7 +244,7 @@ class HeartFChatting: ) # logger.info(self.last_read_time) # logger.info(new_message) - return True,total_interest/new_message_count + return True, total_interest / new_message_count if new_message_count > 0 else 0.0 # 检查累计兴趣值 if new_message_count > 0: @@ -259,7 +259,7 @@ class HeartFChatting: logger.info( f"{self.log_prefix} 累计兴趣值达到{total_interest:.2f}(>{modified_exit_interest_threshold:.1f})，结束等待" ) - return True,total_interest/new_message_count + return True, total_interest / new_message_count if new_message_count > 0 else 0.0 # 每10秒输出一次等待状态 if int(time.time() - self.last_read_time) > 0 and int(time.time() - self.last_read_time) % 15 == 0: @@ -302,10 +302,15 @@ class HeartFChatting: cycle_timers: Dict[str, float], thinking_id, actions, + selected_expressions:List[int] = None, ) -> Tuple[Dict[str, Any], str, Dict[str, float]]: with Timer("回复发送", cycle_timers): - reply_text = await self._send_response(response_set, action_message) + reply_text = await self._send_response( + reply_set=response_set, + message_data=action_message, + selected_expressions=selected_expressions, + ) # 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值 platform = action_message.get("chat_info_platform") @@ -474,7 +479,7 @@ class HeartFChatting: else: try: - success, response_set, _ = await generator_api.generate_reply( + success, response_set, prompt_selected_expressions = await generator_api.generate_reply( chat_stream=self.chat_stream, reply_message = action_info["action_message"], available_actions=available_actions, @@ -483,7 +488,13 @@ class HeartFChatting: enable_tool=global_config.tool.enable_tool, request_type="replyer", from_plugin=False, + return_expressions=True, ) + + if prompt_selected_expressions and len(prompt_selected_expressions) > 1: + _,selected_expressions = prompt_selected_expressions + else: + selected_expressions = [] if not success or not response_set: logger.info(f"对 {action_info['action_message'].get('processed_plain_text')} 的回复生成失败") @@ -504,11 +515,12 @@ class HeartFChatting: } loop_info, reply_text, cycle_timers_reply = await self._send_and_store_reply( - response_set, - action_info["action_message"], - cycle_timers, - thinking_id, - actions, + response_set=response_set, + action_message=action_info["action_message"], + cycle_timers=cycle_timers, + thinking_id=thinking_id, + actions=actions, + selected_expressions=selected_expressions, ) return { "action_type": "reply", @@ -685,7 +697,11 @@ class HeartFChatting: traceback.print_exc() return False, "", "" - async def _send_response(self, reply_set, message_data) -> str: + async def _send_response(self, + reply_set, + message_data, + selected_expressions:List[int] = None, + ) -> str: new_message_count = message_api.count_new_messages( chat_id=self.chat_stream.stream_id, start_time=self.last_read_time, end_time=time.time() ) @@ -706,6 +722,7 @@ class HeartFChatting: reply_message = message_data, set_reply=need_reply, typing=False, + selected_expressions=selected_expressions, ) first_replied = True else: @@ -715,6 +732,7 @@ class HeartFChatting: reply_message = message_data, set_reply=False, typing=True, + selected_expressions=selected_expressions, ) reply_text += data diff --git a/src/chat/express/expression_selector.py b/src/chat/express/expression_selector.py index f24f794b9..64a64cd26 100644 --- a/src/chat/express/expression_selector.py +++ b/src/chat/express/expression_selector.py @@ -137,6 +137,7 @@ class ExpressionSelector: style_exprs = [ { + "id": expr.id, "situation": expr.situation, "style": expr.style, "count": expr.count, @@ -203,14 +204,14 @@ class ExpressionSelector: # 检查是否允许在此聊天流中使用表达 if not self.can_use_expression_for_chat(chat_id): logger.debug(f"聊天流 {chat_id} 不允许使用表达，返回空列表") - return [] + return [], [] # 1. 获取20个随机表达方式（现在按权重抽取） style_exprs = self.get_random_expressions(chat_id, 10) - if len(style_exprs) < 20: + if len(style_exprs) < 10: logger.info(f"聊天流 {chat_id} 表达方式正在积累中") - return [] + return [], [] # 2. 构建所有表达方式的索引和情境列表 all_expressions = [] @@ -218,15 +219,13 @@ class ExpressionSelector: # 添加style表达方式 for expr in style_exprs: - if isinstance(expr, dict) and "situation" in expr and "style" in expr: - expr_with_type = expr.copy() - expr_with_type["type"] = "style" - all_expressions.append(expr_with_type) - all_situations.append(f"{len(all_expressions)}.当 {expr['situation']} 时，使用 {expr['style']}") + expr = expr.copy() + all_expressions.append(expr) + all_situations.append(f"{len(all_expressions)}.当 {expr['situation']} 时，使用 {expr['style']}") if not all_expressions: logger.warning("没有找到可用的表达方式") - return [] + return [], [] all_situations_str = "\n".join(all_situations) @@ -247,8 +246,6 @@ class ExpressionSelector: target_message_extra_block=target_message_extra_block, ) - print(prompt) - # 4. 调用LLM try: @@ -265,7 +262,7 @@ class ExpressionSelector: if not content: logger.warning("LLM返回空结果") - return [] + return [], [] # 5. 解析结果 result = repair_json(content) @@ -275,15 +272,17 @@ class ExpressionSelector: if not isinstance(result, dict) or "selected_situations" not in result: logger.error("LLM返回格式错误") logger.info(f"LLM返回结果: \n{content}") - return [] + return [], [] selected_indices = result["selected_situations"] # 根据索引获取完整的表达方式 valid_expressions = [] + selected_ids = [] for idx in selected_indices: if isinstance(idx, int) and 1 <= idx <= len(all_expressions): expression = all_expressions[idx - 1] # 索引从1开始 + selected_ids.append(expression["id"]) valid_expressions.append(expression) # 对选中的所有表达方式，一次性更新count数 @@ -291,11 +290,11 @@ class ExpressionSelector: self.update_expressions_count_batch(valid_expressions, 0.006) # logger.info(f"LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个") - return valid_expressions + return valid_expressions , selected_ids except Exception as e: logger.error(f"LLM处理表达方式选择时出错: {e}") - return [] + return [], [] diff --git a/src/chat/express/expression_selector_new.py b/src/chat/express/expression_selector_new.py deleted file mode 100644 index 97026712e..000000000 --- a/src/chat/express/expression_selector_new.py +++ /dev/null @@ -1,298 +0,0 @@ -import json -import time -import random -import hashlib - -from typing import List, Dict, Tuple, Optional, Any -from json_repair import repair_json - -from src.llm_models.utils_model import LLMRequest -from src.config.config import global_config, model_config -from src.common.logger import get_logger -from src.common.database.database_model import Expression -from src.chat.utils.prompt_builder import Prompt, global_prompt_manager - -logger = get_logger("expression_selector") - - -def init_prompt(): - expression_evaluation_prompt = """ -以下是正在进行的聊天内容： -{chat_observe_info} - -你的名字是{bot_name}{target_message} - -你知道以下这些表达方式，梗和说话方式： -{all_situations} - -现在，请你根据聊天记录从中挑选合适的表达方式，梗和说话方式，组织一条回复风格指导，指导的目的是在组织回复的时候提供一些语言风格和梗上的参考。 -请在reply_style_guide中以平文本输出指导，不要浮夸，并在selected_expressions中说明在指导中你挑选了哪些表达方式，梗和说话方式，以json格式输出： -例子： -{{ - "reply_style_guide": "...", - "selected_expressions": [2, 3, 4, 7] -}} -请严格按照JSON格式输出，不要包含其他内容： -""" - Prompt(expression_evaluation_prompt, "expression_evaluation_prompt") - - -def weighted_sample(population: List[Dict], weights: List[float], k: int) -> List[Dict]: - """按权重随机抽样""" - if not population or not weights or k <= 0: - return [] - - if len(population) <= k: - return population.copy() - - # 使用累积权重的方法进行加权抽样 - selected = [] - population_copy = population.copy() - weights_copy = weights.copy() - - for _ in range(k): - if not population_copy: - break - - # 选择一个元素 - chosen_idx = random.choices(range(len(population_copy)), weights=weights_copy)[0] - selected.append(population_copy.pop(chosen_idx)) - weights_copy.pop(chosen_idx) - - return selected - - -class ExpressionSelector: - def __init__(self): - self.llm_model = LLMRequest( - model_set=model_config.model_task_config.utils_small, request_type="expression.selector" - ) - - def can_use_expression_for_chat(self, chat_id: str) -> bool: - """ - 检查指定聊天流是否允许使用表达 - - Args: - chat_id: 聊天流ID - - Returns: - bool: 是否允许使用表达 - """ - try: - use_expression, _, _ = global_config.expression.get_expression_config_for_chat(chat_id) - return use_expression - except Exception as e: - logger.error(f"检查表达使用权限失败: {e}") - return False - - @staticmethod - def _parse_stream_config_to_chat_id(stream_config_str: str) -> Optional[str]: - """解析'platform:id:type'为chat_id（与get_stream_id一致）""" - try: - parts = stream_config_str.split(":") - if len(parts) != 3: - return None - platform = parts[0] - id_str = parts[1] - stream_type = parts[2] - is_group = stream_type == "group" - if is_group: - components = [platform, str(id_str)] - else: - components = [platform, str(id_str), "private"] - key = "_".join(components) - return hashlib.md5(key.encode()).hexdigest() - except Exception: - return None - - def get_related_chat_ids(self, chat_id: str) -> List[str]: - """根据expression_groups配置，获取与当前chat_id相关的所有chat_id（包括自身）""" - groups = global_config.expression.expression_groups - for group in groups: - group_chat_ids = [] - for stream_config_str in group: - if chat_id_candidate := self._parse_stream_config_to_chat_id(stream_config_str): - group_chat_ids.append(chat_id_candidate) - if chat_id in group_chat_ids: - return group_chat_ids - return [chat_id] - - def get_random_expressions( - self, chat_id: str, total_num: int - ) -> List[Dict[str, Any]]: - # sourcery skip: extract-duplicate-method, move-assign - # 支持多chat_id合并抽选 - related_chat_ids = self.get_related_chat_ids(chat_id) - - # 优化：一次性查询所有相关chat_id的表达方式 - style_query = Expression.select().where( - (Expression.chat_id.in_(related_chat_ids)) & (Expression.type == "style") - ) - - style_exprs = [ - { - "situation": expr.situation, - "style": expr.style, - "count": expr.count, - "last_active_time": expr.last_active_time, - "source_id": expr.chat_id, - "type": "style", - "create_date": expr.create_date if expr.create_date is not None else expr.last_active_time, - } - for expr in style_query - ] - - # 按权重抽样（使用count作为权重） - if style_exprs: - style_weights = [expr.get("count", 1) for expr in style_exprs] - selected_style = weighted_sample(style_exprs, style_weights, total_num) - else: - selected_style = [] - return selected_style - - def update_expressions_count_batch(self, expressions_to_update: List[Dict[str, Any]], increment: float = 0.1): - """对一批表达方式更新count值，按chat_id+type分组后一次性写入数据库""" - if not expressions_to_update: - return - updates_by_key = {} - for expr in expressions_to_update: - source_id: str = expr.get("source_id") # type: ignore - expr_type: str = expr.get("type", "style") - situation: str = expr.get("situation") # type: ignore - style: str = expr.get("style") # type: ignore - if not source_id or not situation or not style: - logger.warning(f"表达方式缺少必要字段，无法更新: {expr}") - continue - key = (source_id, expr_type, situation, style) - if key not in updates_by_key: - updates_by_key[key] = expr - for chat_id, expr_type, situation, style in updates_by_key: - query = Expression.select().where( - (Expression.chat_id == chat_id) - & (Expression.type == expr_type) - & (Expression.situation == situation) - & (Expression.style == style) - ) - if query.exists(): - expr_obj = query.get() - current_count = expr_obj.count - new_count = min(current_count + increment, 5.0) - expr_obj.count = new_count - expr_obj.last_active_time = time.time() - expr_obj.save() - logger.debug( - f"表达方式激活: 原count={current_count:.3f}, 增量={increment}, 新count={new_count:.3f} in db" - ) - - async def select_suitable_expressions_llm( - self, - chat_id: str, - chat_info: str, - max_num: int = 10, - target_message: Optional[str] = None, - ) -> Tuple[str, List[Dict[str, Any]]]: - # sourcery skip: inline-variable, list-comprehension - """使用LLM选择适合的表达方式""" - - # 检查是否允许在此聊天流中使用表达 - if not self.can_use_expression_for_chat(chat_id): - logger.debug(f"聊天流 {chat_id} 不允许使用表达，返回空列表") - return "", [] - - # 1. 获取20个随机表达方式（现在按权重抽取） - style_exprs = self.get_random_expressions(chat_id, 10) - - # 2. 构建所有表达方式的索引和情境列表 - all_expressions = [] - all_situations = [] - - # 添加style表达方式 - for expr in style_exprs: - if isinstance(expr, dict) and "situation" in expr and "style" in expr: - expr_with_type = expr.copy() - expr_with_type["type"] = "style" - all_expressions.append(expr_with_type) - all_situations.append(f"{len(all_expressions)}.当 {expr['situation']} 时，使用 {expr['style']}") - - if not all_expressions: - logger.warning("没有找到可用的表达方式") - return "", [] - - all_situations_str = "\n".join(all_situations) - - if target_message: - target_message_str = f"，现在你想要回复消息：{target_message}" - target_message_extra_block = "4.考虑你要回复的目标消息" - else: - target_message_str = "" - target_message_extra_block = "" - - # 3. 构建prompt（只包含情境，不包含完整的表达方式） - prompt = (await global_prompt_manager.get_prompt_async("expression_evaluation_prompt")).format( - bot_name=global_config.bot.nickname, - chat_observe_info=chat_info, - all_situations=all_situations_str, - max_num=max_num, - target_message=target_message_str, - target_message_extra_block=target_message_extra_block, - ) - - print(prompt) - - # 4. 调用LLM - try: - - # start_time = time.time() - content, (reasoning_content, model_name, _) = await self.llm_model.generate_response_async(prompt=prompt) - # logger.info(f"LLM请求时间: {model_name} {time.time() - start_time} \n{prompt}") - - # logger.info(f"模型名称: {model_name}") - logger.info(f"LLM返回结果: {content}") - # if reasoning_content: - # logger.info(f"LLM推理: {reasoning_content}") - # else: - # logger.info(f"LLM推理: 无") - - if not content: - logger.warning("LLM返回空结果") - return "", [] - - # 5. 解析结果 - result = repair_json(content) - if isinstance(result, str): - result = json.loads(result) - - if not isinstance(result, dict) or "reply_style_guide" not in result or "selected_expressions" not in result: - logger.error("LLM返回格式错误") - logger.info(f"LLM返回结果: \n{content}") - return "", [] - - reply_style_guide = result["reply_style_guide"] - selected_expressions = result["selected_expressions"] - - # 根据索引获取完整的表达方式 - valid_expressions = [] - for idx in selected_expressions: - if isinstance(idx, int) and 1 <= idx <= len(all_expressions): - expression = all_expressions[idx - 1] # 索引从1开始 - valid_expressions.append(expression) - - # 对选中的所有表达方式，一次性更新count数 - if valid_expressions: - self.update_expressions_count_batch(valid_expressions, 0.006) - - # logger.info(f"LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个") - return reply_style_guide, valid_expressions - - except Exception as e: - logger.error(f"LLM处理表达方式选择时出错: {e}") - return "", [] - - - -init_prompt() - -try: - expression_selector = ExpressionSelector() -except Exception as e: - print(f"ExpressionSelector初始化失败: {e}") diff --git a/src/chat/message_receive/message.py b/src/chat/message_receive/message.py index bf4430876..3fb4e5c3a 100644 --- a/src/chat/message_receive/message.py +++ b/src/chat/message_receive/message.py @@ -4,7 +4,7 @@ import urllib3 from abc import abstractmethod from dataclasses import dataclass from rich.traceback import install -from typing import Optional, Any +from typing import Optional, Any, List from maim_message import Seg, UserInfo, BaseMessageInfo, MessageBase from src.common.logger import get_logger @@ -421,6 +421,7 @@ class MessageSending(MessageProcessBase): thinking_start_time: float = 0, apply_set_reply_logic: bool = False, reply_to: Optional[str] = None, + selected_expressions:List[int] = None, ): # 调用父类初始化 super().__init__( @@ -445,6 +446,8 @@ class MessageSending(MessageProcessBase): self.display_message = display_message self.interest_value = 0.0 + + self.selected_expressions = selected_expressions def build_reply(self): """设置回复消息""" diff --git a/src/chat/message_receive/storage.py b/src/chat/message_receive/storage.py index ab5c18336..e8d4b6bb3 100644 --- a/src/chat/message_receive/storage.py +++ b/src/chat/message_receive/storage.py @@ -65,6 +65,7 @@ class MessageStorage: is_command = False key_words = "" key_words_lite = "" + selected_expressions = message.selected_expressions else: filtered_display_message = "" interest_value = message.interest_value @@ -79,6 +80,7 @@ class MessageStorage: # 序列化关键词列表为JSON字符串 key_words = MessageStorage._serialize_keywords(message.key_words) key_words_lite = MessageStorage._serialize_keywords(message.key_words_lite) + selected_expressions = "" chat_info_dict = chat_stream.to_dict() user_info_dict = message.message_info.user_info.to_dict() # type: ignore @@ -127,6 +129,7 @@ class MessageStorage: is_command=is_command, key_words=key_words, key_words_lite=key_words_lite, + selected_expressions=selected_expressions, ) except Exception: logger.exception("存储消息失败") diff --git a/src/chat/replyer/default_generator.py b/src/chat/replyer/default_generator.py index b51e6a9f8..3610cc9b1 100644 --- a/src/chat/replyer/default_generator.py +++ b/src/chat/replyer/default_generator.py @@ -162,7 +162,7 @@ class DefaultReplyer: from_plugin: bool = True, stream_id: Optional[str] = None, reply_message: Optional[Dict[str, Any]] = None, - ) -> Tuple[bool, Optional[Dict[str, Any]], Optional[str]]: + ) -> Tuple[bool, Optional[Dict[str, Any]], Optional[str], List[Dict[str, Any]]]: # sourcery skip: merge-nested-ifs """ 回复器 (Replier): 负责生成回复文本的核心逻辑。 @@ -186,7 +186,7 @@ class DefaultReplyer: try: # 3. 构建 Prompt with Timer("构建Prompt", {}): # 内部计时器，可选保留 - prompt = await self.build_prompt_reply_context( + prompt,selected_expressions = await self.build_prompt_reply_context( extra_info=extra_info, available_actions=available_actions, choosen_actions=choosen_actions, @@ -197,7 +197,7 @@ class DefaultReplyer: if not prompt: logger.warning("构建prompt失败，跳过回复生成") - return False, None, None + return False, None, None, [] from src.plugin_system.core.events_manager import events_manager if not from_plugin: @@ -229,16 +229,16 @@ class DefaultReplyer: except Exception as llm_e: # 精简报错信息 logger.error(f"LLM 生成失败: {llm_e}") - return False, None, prompt # LLM 调用失败则无法生成回复 + return False, None, prompt, selected_expressions # LLM 调用失败则无法生成回复 - return True, llm_response, prompt + return True, llm_response, prompt, selected_expressions except UserWarning as uw: raise uw except Exception as e: logger.error(f"回复生成意外失败: {e}") traceback.print_exc() - return False, None, prompt + return False, None, prompt, selected_expressions async def rewrite_reply_with_context( self, @@ -302,7 +302,7 @@ class DefaultReplyer: return person.build_relationship(points_num=5) - async def build_expression_habits(self, chat_history: str, target: str) -> str: + async def build_expression_habits(self, chat_history: str, target: str) -> Tuple[str, List[int]]: """构建表达习惯块 Args: @@ -315,11 +315,11 @@ class DefaultReplyer: # 检查是否允许在此聊天流中使用表达 use_expression, _, _ = global_config.expression.get_expression_config_for_chat(self.chat_stream.stream_id) if not use_expression: - return "" + return "", [] style_habits = [] # 使用从处理器传来的选中表达方式 # LLM模式：调用LLM选择5-10个，然后随机选5个 - selected_expressions = await expression_selector.select_suitable_expressions_llm( + selected_expressions, selected_ids = await expression_selector.select_suitable_expressions_llm( self.chat_stream.stream_id, chat_history, max_num=8, target_message=target ) @@ -343,7 +343,7 @@ class DefaultReplyer: ) expression_habits_block += f"{style_habits_str}\n" - return f"{expression_habits_title}\n{expression_habits_block}" + return f"{expression_habits_title}\n{expression_habits_block}", selected_ids async def build_memory_block(self, chat_history: str, target: str) -> str: """构建记忆块 @@ -636,9 +636,8 @@ class DefaultReplyer: action_descriptions += f"- {action_name}: {action_description}\n" action_descriptions += "\n" + choosen_action_descriptions = "" if choosen_actions: - action_descriptions += "根据聊天情况，你决定在回复的同时做以下这些动作：\n" - for action in choosen_actions: action_name = action.get('action_type', 'unknown_action') if action_name =="reply": @@ -646,9 +645,11 @@ class DefaultReplyer: action_description = action.get('reason', '无描述') reasoning = action.get('reasoning', '无原因') - - action_descriptions += f"- {action_name}: {action_description}，原因：{reasoning}\n" + choosen_action_descriptions += f"- {action_name}: {action_description}，原因：{reasoning}\n" + if choosen_action_descriptions: + action_descriptions += "根据聊天情况，你决定在回复的同时做以下这些动作：\n" + action_descriptions += choosen_action_descriptions return action_descriptions @@ -661,7 +662,7 @@ class DefaultReplyer: choosen_actions: Optional[List[Dict[str, Any]]] = None, enable_tool: bool = True, reply_message: Optional[Dict[str, Any]] = None, - ) -> str: + ) -> Tuple[str, List[int]]: """ 构建回复器上下文 @@ -759,7 +760,7 @@ class DefaultReplyer: logger.warning(f"回复生成前信息获取耗时过长: {chinese_name} 耗时: {duration:.1f}s，请使用更快的模型") logger.info(f"在回复前的步骤耗时: {'; '.join(timing_logs)}") - expression_habits_block = results_dict["expression_habits"] + expression_habits_block, selected_expressions = results_dict["expression_habits"] relation_info = results_dict["relation_info"] memory_block = results_dict["memory_block"] tool_info = results_dict["tool_info"] @@ -831,7 +832,7 @@ class DefaultReplyer: reply_style=global_config.personality.reply_style, keywords_reaction_prompt=keywords_reaction_prompt, moderation_prompt=moderation_prompt_block, - ) + ),selected_expressions else: return await global_prompt_manager.format_prompt( "replyer_prompt", @@ -852,7 +853,7 @@ class DefaultReplyer: reply_style=global_config.personality.reply_style, keywords_reaction_prompt=keywords_reaction_prompt, moderation_prompt=moderation_prompt_block, - ) + ),selected_expressions async def build_prompt_rewrite_context( self, @@ -860,7 +861,7 @@ class DefaultReplyer: reason: str, reply_to: str, reply_message: Optional[Dict[str, Any]] = None, - ) -> str: # sourcery skip: merge-else-if-into-elif, remove-redundant-if + ) -> Tuple[str, List[int]]: # sourcery skip: merge-else-if-into-elif, remove-redundant-if chat_stream = self.chat_stream chat_id = chat_stream.stream_id is_group_chat = bool(chat_stream.group_info) @@ -893,7 +894,7 @@ class DefaultReplyer: ) # 并行执行2个构建任务 - expression_habits_block, relation_info = await asyncio.gather( + (expression_habits_block, selected_expressions), relation_info = await asyncio.gather( self.build_expression_habits(chat_talking_prompt_half, target), self.build_relation_info(sender, target), ) diff --git a/src/common/database/database_model.py b/src/common/database/database_model.py index 6055b7724..e08c82f7f 100644 --- a/src/common/database/database_model.py +++ b/src/common/database/database_model.py @@ -169,6 +169,8 @@ class Messages(BaseModel): is_picid = BooleanField(default=False) is_command = BooleanField(default=False) is_notify = BooleanField(default=False) + + selected_expressions = TextField(null=True) class Meta: # database = db # 继承自 BaseModel diff --git a/src/mais4u/mais4u_chat/s4u_prompt.py b/src/mais4u/mais4u_chat/s4u_prompt.py index 8fb3eb155..5f1d1ce5b 100644 --- a/src/mais4u/mais4u_chat/s4u_prompt.py +++ b/src/mais4u/mais4u_chat/s4u_prompt.py @@ -102,7 +102,7 @@ class PromptBuilder: # 使用从处理器传来的选中表达方式 # LLM模式：调用LLM选择5-10个，然后随机选5个 - selected_expressions = await expression_selector.select_suitable_expressions_llm( + selected_expressions ,_ = await expression_selector.select_suitable_expressions_llm( chat_stream.stream_id, chat_history, max_num=12, target_message=target ) diff --git a/src/mood/mood_manager.py b/src/mood/mood_manager.py index 036ea0f82..b70d99b34 100644 --- a/src/mood/mood_manager.py +++ b/src/mood/mood_manager.py @@ -192,7 +192,7 @@ class ChatMood: class MoodRegressionTask(AsyncTask): def __init__(self, mood_manager: "MoodManager"): - super().__init__(task_name="MoodRegressionTask", run_interval=30) + super().__init__(task_name="MoodRegressionTask", run_interval=45) self.mood_manager = mood_manager async def run(self): @@ -202,8 +202,8 @@ class MoodRegressionTask(AsyncTask): if mood.last_change_time == 0: continue - if now - mood.last_change_time > 180: - if mood.regression_count >= 3: + if now - mood.last_change_time > 200: + if mood.regression_count >= 2: continue logger.debug(f"{mood.log_prefix} 开始情绪回归, 第 {mood.regression_count + 1} 次") diff --git a/src/person_info/relationship_manager.py b/src/person_info/relationship_manager.py index 35d7079b2..c7ee155ee 100644 --- a/src/person_info/relationship_manager.py +++ b/src/person_info/relationship_manager.py @@ -47,7 +47,7 @@ def init_prompt(): }} ] -如果没有，就只输出空数组：[] +如果没有，就只输出空json：{{}} """, "relation_points", ) @@ -77,7 +77,7 @@ def init_prompt(): "attitude": 0, "confidence": 0.5 }} -如果无法看出对方对你的态度，就只输出空数组：[] +如果无法看出对方对你的态度，就只输出空数组：{{}} 现在，请你输出: """, @@ -111,7 +111,7 @@ def init_prompt(): "neuroticism": 0, "confidence": 0.5 }} -如果无法看出对方的神经质程度，就只输出空数组：[] +如果无法看出对方的神经质程度，就只输出空数组：{{}} 现在，请你输出: """, @@ -163,7 +163,7 @@ class RelationshipManager: points_data = json.loads(points) # 只处理正确的格式，错误格式直接跳过 - if points_data == "none" or not points_data or (isinstance(points_data, str) and points_data.lower() == "none") or (isinstance(points_data, list) and len(points_data) == 0): + if not points_data or (isinstance(points_data, list) and len(points_data) == 0): points_list = [] elif isinstance(points_data, list): points_list = [(item["point"], float(item["weight"]), current_time) for item in points_data] @@ -263,7 +263,7 @@ class RelationshipManager: attitude = repair_json(attitude) attitude_data = json.loads(attitude) - if attitude_data == "none" or not attitude_data or (isinstance(attitude_data, str) and attitude_data.lower() == "none") or (isinstance(attitude_data, list) and len(attitude_data) == 0): + if not attitude_data or (isinstance(attitude_data, list) and len(attitude_data) == 0): return "" # 确保 attitude_data 是字典格式 @@ -309,7 +309,7 @@ class RelationshipManager: neuroticism = repair_json(neuroticism) neuroticism_data = json.loads(neuroticism) - if neuroticism_data == "none" or not neuroticism_data or (isinstance(neuroticism_data, str) and neuroticism_data.lower() == "none") or (isinstance(neuroticism_data, list) and len(neuroticism_data) == 0): + if not neuroticism_data or (isinstance(neuroticism_data, list) and len(neuroticism_data) == 0): return "" # 确保 neuroticism_data 是字典格式 diff --git a/src/plugin_system/apis/generator_api.py b/src/plugin_system/apis/generator_api.py index 4298a5f11..b693350b0 100644 --- a/src/plugin_system/apis/generator_api.py +++ b/src/plugin_system/apis/generator_api.py @@ -84,7 +84,8 @@ async def generate_reply( return_prompt: bool = False, request_type: str = "generator_api", from_plugin: bool = True, -) -> Tuple[bool, List[Tuple[str, Any]], Optional[str]]: + return_expressions: bool = False, +) -> Tuple[bool, List[Tuple[str, Any]], Optional[Tuple[str, List[Dict[str, Any]]]]]: """生成回复 Args: @@ -123,7 +124,7 @@ async def generate_reply( reply_reason = action_data.get("reason", "") # 调用回复器生成回复 - success, llm_response_dict, prompt = await replyer.generate_reply_with_context( + success, llm_response_dict, prompt, selected_expressions = await replyer.generate_reply_with_context( extra_info=extra_info, available_actions=available_actions, choosen_actions=choosen_actions, @@ -144,10 +145,16 @@ async def generate_reply( logger.debug(f"[GeneratorAPI] 回复生成成功，生成了 {len(reply_set)} 个回复项") if return_prompt: - return success, reply_set, prompt + if return_expressions: + return success, reply_set, (prompt, selected_expressions) + else: + return success, reply_set, prompt else: - return success, reply_set, None - + if return_expressions: + return success, reply_set, (None, selected_expressions) + else: + return success, reply_set, None + except ValueError as ve: raise ve diff --git a/src/plugin_system/apis/send_api.py b/src/plugin_system/apis/send_api.py index 870c979f7..700042ded 100644 --- a/src/plugin_system/apis/send_api.py +++ b/src/plugin_system/apis/send_api.py @@ -21,7 +21,7 @@ import traceback import time -from typing import Optional, Union, Dict, Any +from typing import Optional, Union, Dict, Any, List from src.common.logger import get_logger # 导入依赖 @@ -49,6 +49,7 @@ async def _send_to_target( reply_message: Optional[Dict[str, Any]] = None, storage_message: bool = True, show_log: bool = True, + selected_expressions:List[int] = None, ) -> bool: """向指定目标发送消息的内部实现 @@ -121,6 +122,7 @@ async def _send_to_target( is_emoji=(message_type == "emoji"), thinking_start_time=current_time, reply_to=reply_to_platform_id, + selected_expressions=selected_expressions, ) # 发送消息 @@ -208,6 +210,7 @@ async def text_to_stream( set_reply: bool = False, reply_message: Optional[Dict[str, Any]] = None, storage_message: bool = True, + selected_expressions:List[int] = None, ) -> bool: """向指定流发送文本消息 @@ -230,6 +233,7 @@ async def text_to_stream( set_reply=set_reply, reply_message=reply_message, storage_message=storage_message, + selected_expressions=selected_expressions, )