diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index b90eac426..c511279c9 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -50,8 +50,8 @@ class BotConfig: # 默认人设 PROMPT_PERSONALITY=[ - "曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧，你正在浏览qq群", - "是一个女大学生，你有黑色头发，你会刷小红书, 你正在浏览qq群" + "曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧", + "是一个女大学生，你有黑色头发，你会刷小红书" ] PROMPT_SCHEDULE_GEN="一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书" diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py index cc60f2522..0701af3ec 100644 --- a/src/plugins/chat/llm_generator.py +++ b/src/plugins/chat/llm_generator.py @@ -23,65 +23,96 @@ class ResponseGenerator: def __init__(self): self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7) self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7) - self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7) + self.model_r1_distill = LLM_request( + model=global_config.llm_reasoning_minor, temperature=0.7 + ) self.db = Database.get_instance() - self.current_model_type = 'r1' # 默认使用 R1 + self.current_model_type = "r1" # 默认使用 R1 - async def generate_response(self, message: Message) -> Optional[Union[str, List[str]]]: + async def generate_response( + self, message: Message + ) -> Optional[Union[str, List[str]]]: """根据当前模型类型选择对应的生成函数""" # 从global_config中获取模型概率值并选择模型 rand = random.random() if rand < global_config.MODEL_R1_PROBABILITY: - self.current_model_type = 'r1' + self.current_model_type = "r1" current_model = self.model_r1 - elif rand < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY: - self.current_model_type = 'v3' + elif ( + rand + < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY + ): + self.current_model_type = "v3" current_model = self.model_v3 else: - self.current_model_type = 'r1_distill' + self.current_model_type = "r1_distill" current_model = self.model_r1_distill - print(f"+++++++++++++++++{global_config.BOT_NICKNAME}{self.current_model_type}思考中+++++++++++++++++") - - model_response = await self._generate_response_with_model(message, current_model) - + print( + f"+++++++++++++++++{global_config.BOT_NICKNAME}{self.current_model_type}思考中+++++++++++++++++" + ) + + model_response = await self._generate_response_with_model( + message, current_model + ) + if model_response: - print(f'{global_config.BOT_NICKNAME}的回复是：{model_response}') + print(f"{global_config.BOT_NICKNAME}的回复是：{model_response}") model_response, emotion = await self._process_response(model_response) if model_response: print(f"为 '{model_response}' 获取到的情感标签为：{emotion}") - valuedict={ - 'happy':0.5,'angry':-1,'sad':-0.5,'surprised':0.5,'disgusted':-1.5,'fearful':-0.25,'neutral':0.25 + valuedict = { + "happy": 0.5, + "angry": -1, + "sad": -0.5, + "surprised": 0.5, + "disgusted": -1.5, + "fearful": -0.25, + "neutral": 0.25, } - await relationship_manager.update_relationship_value(message.user_id, relationship_value=valuedict[emotion[0]]) + await relationship_manager.update_relationship_value( + message.user_id, relationship_value=valuedict[emotion[0]] + ) return model_response, emotion return None, [] - async def _generate_response_with_model(self, message: Message, model: LLM_request) -> Optional[str]: + async def _generate_response_with_model( + self, message: Message, model: LLM_request + ) -> Optional[str]: """使用指定的模型生成回复""" sender_name = message.user_nickname or f"用户{message.user_id}" if message.user_cardname: - sender_name=f"[({message.user_id}){message.user_nickname}]{message.user_cardname}" - + sender_name = ( + f"[({message.user_id}){message.user_nickname}]{message.user_cardname}" + ) + # 获取关系值 - relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value if relationship_manager.get_relationship(message.user_id) else 0.0 + relationship_value = ( + relationship_manager.get_relationship(message.user_id).relationship_value + if relationship_manager.get_relationship(message.user_id) + else 0.0 + ) if relationship_value != 0.0: - print(f"\033[1;32m[关系管理]\033[0m 回复中_当前关系值: {relationship_value}") - + print( + f"\033[1;32m[关系管理]\033[0m 回复中_当前关系值: {relationship_value}" + ) + # 构建prompt prompt, prompt_check = prompt_builder._build_prompt( message_txt=message.processed_plain_text, sender_name=sender_name, relationship_value=relationship_value, - group_id=message.group_id + group_id=message.group_id, ) # 读空气模块 if global_config.enable_kuuki_read: - content_check, reasoning_content_check = await self.model_v3.generate_response(prompt_check) + content_check, reasoning_content_check = ( + await self.model_v3.generate_response(prompt_check) + ) print(f"\033[1;32m[读空气]\033[0m 读空气结果为{content_check}") - if 'yes' not in content_check.lower() and random.random() < 0.3: + if "yes" not in content_check.lower() and random.random() < 0.3: self._save_to_db( message=message, sender_name=sender_name, @@ -90,13 +121,13 @@ class ResponseGenerator: content="", content_check=content_check, reasoning_content="", - reasoning_content_check=reasoning_content_check + reasoning_content_check=reasoning_content_check, ) return None # 生成回复 content, reasoning_content = await model.generate_response(prompt) - + # 保存到数据库 self._save_to_db( message=message, @@ -106,50 +137,101 @@ class ResponseGenerator: content=content, content_check=content_check if global_config.enable_kuuki_read else "", reasoning_content=reasoning_content, - reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else "" + reasoning_content_check=( + reasoning_content_check if global_config.enable_kuuki_read else "" + ), ) - + return content - def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str, - content: str, content_check: str, reasoning_content: str, reasoning_content_check: str): + def _save_to_db( + self, + message: Message, + sender_name: str, + prompt: str, + prompt_check: str, + content: str, + content_check: str, + reasoning_content: str, + reasoning_content_check: str, + ): """保存对话记录到数据库""" - self.db.db.reasoning_logs.insert_one({ - 'time': time.time(), - 'group_id': message.group_id, - 'user': sender_name, - 'message': message.processed_plain_text, - 'model': self.current_model_type, - 'reasoning_check': reasoning_content_check, - 'response_check': content_check, - 'reasoning': reasoning_content, - 'response': content, - 'prompt': prompt, - 'prompt_check': prompt_check - }) + self.db.db.reasoning_logs.insert_one( + { + "time": time.time(), + "group_id": message.group_id, + "user": sender_name, + "message": message.processed_plain_text, + "model": self.current_model_type, + "reasoning_check": reasoning_content_check, + "response_check": content_check, + "reasoning": reasoning_content, + "response": content, + "prompt": prompt, + "prompt_check": prompt_check, + } + ) async def _get_emotion_tags(self, content: str) -> List[str]: """提取情感标签""" try: - prompt = f'''请从以下内容中，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签并输出 + prompt = f"""请从以下内容中，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签并输出 只输出标签就好，不要输出其他内容: 内容：{content} 输出： - ''' - + """ + content, _ = await self.model_v3.generate_response(prompt) return [content.strip()] if content else ["neutral"] - + except Exception as e: print(f"获取情感标签时出错: {e}") return ["neutral"] - + async def _process_response(self, content: str) -> Tuple[List[str], List[str]]: """处理响应内容，返回处理后的内容和情感标签""" if not content: return None, [] - + emotion_tags = await self._get_emotion_tags(content) processed_response = process_llm_response(content) - - return processed_response, emotion_tags \ No newline at end of file + + return processed_response, emotion_tags + + +class InitiativeMessageGenerate: + def __init__(self): + self.db = Database.get_instance() + self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7) + self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7) + self.model_r1_distill = LLM_request( + model=global_config.llm_reasoning_minor, temperature=0.7 + ) + + def gen_response(self, message: Message): + topic_select_prompt, dots_for_select, prompt_template = ( + prompt_builder._build_initiative_prompt_select(message.group_id) + ) + content_select, reasoning = self.model_v3.generate_response(topic_select_prompt) + print(f"[DEBUG] {content_select} {reasoning}") + topics_list = [dot[0] for dot in dots_for_select] + if content_select: + if content_select in topics_list: + select_dot = dots_for_select[topics_list.index(content_select)] + else: + return None + else: + return None + prompt_check, memory = prompt_builder._build_initiative_prompt_check( + select_dot[1], prompt_template + ) + content_check, reasoning_check = self.model_v3.generate_response(prompt_check) + print(f"[DEBUG] {content_check} {reasoning_check}") + if "yes" not in content_check.lower(): + return None + prompt = prompt_builder._build_initiative_prompt( + select_dot, prompt_template, memory + ) + content, reasoning = self.model_r1.generate_response(prompt) + print(f"[DEBUG] {content} {reasoning}") + return content diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index eb3c04347..fb630cbeb 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -151,12 +151,11 @@ class PromptBuilder: prompt_personality = '' personality_choice = random.random() if personality_choice < 4/6: # 第一种人格 - prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]},{promt_info_prompt}, + prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}, 你正在浏览qq群,{promt_info_prompt}, 现在请你给出日常且口语化的回复，平淡一些，尽量简短一些。{is_bot_prompt} 请注意把握群里的聊天内容，不要刻意突出自身学科背景，不要回复的太有条理，可以有个性。''' elif personality_choice < 1: # 第二种人格 - prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}，{promt_info_prompt}, - + prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}, 你正在浏览qq群，{promt_info_prompt}, 现在请你给出日常且口语化的回复，请表现你自己的见解，不要一昧迎合，尽量简短一些。{is_bot_prompt} 请你表达自己的见解和观点。可以有个性。''' @@ -196,13 +195,65 @@ class PromptBuilder: prompt_personality_check = '' extra_check_info=f"请注意把握群里的聊天内容的基础上，综合群内的氛围，例如，和{global_config.BOT_NICKNAME}相关的话题要积极回复,如果是at自己的消息一定要回复，如果自己正在和别人聊天一定要回复，其他话题如果合适搭话也可以回复，如果认为应该回复请输出yes，否则输出no，请注意是决定是否需要回复，而不是编写回复内容，除了yes和no不要输出任何回复内容。" if personality_choice < 4/6: # 第一种人格 - prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}，{promt_info_prompt} {activate_prompt_check} {extra_check_info}''' + prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}''' elif personality_choice < 1: # 第二种人格 - prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}，{promt_info_prompt} {activate_prompt_check} {extra_check_info}''' + prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}''' prompt_check_if_response=f"{prompt_info}\n{prompt_date}\n{chat_talking_prompt}\n{prompt_personality_check}" return prompt,prompt_check_if_response + + def _build_initiative_prompt_select(self,group_id): + current_date = time.strftime("%Y-%m-%d", time.localtime()) + current_time = time.strftime("%H:%M:%S", time.localtime()) + bot_schedule_now_time,bot_schedule_now_activity = bot_schedule.get_current_task() + prompt_date = f'''今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n''' + + chat_talking_prompt = '' + if group_id: + chat_talking_prompt = get_recent_group_detailed_plain_text(self.db, group_id, limit=global_config.MAX_CONTEXT_SIZE,combine = True) + + chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}" + # print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}") + + # 获取主动发言的话题 + all_nodes=memory_graph.dots + all_nodes=filter(lambda dot:len(dot[1]['memory_items'])>3,all_nodes) + nodes_for_select=random.sample(all_nodes,5) + topics=[info[0] for info in nodes_for_select] + infos=[info[1] for info in nodes_for_select] + + #激活prompt构建 + activate_prompt = '' + activate_prompt = f"以上是群里正在进行的聊天。" + personality=global_config.PROMPT_PERSONALITY + prompt_personality = '' + personality_choice = random.random() + if personality_choice < 4/6: # 第一种人格 + prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}''' + elif personality_choice < 1: # 第二种人格 + prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}''' + + topics_str=','.join(f"\"{topics}\"") + prompt_for_select=f"你现在想在群里发言，回忆了一下，想到几个话题，分别是{topics_str}，综合当前状态以及群内气氛，请你在其中选择一个合适的话题，注意只需要输出话题，除了话题什么也不要输出(双引号也不要输出)" + + prompt_initiative_select=f"{prompt_date}\n{prompt_personality}\n{prompt_for_select}" + prompt_regular=f"{prompt_date}\n{prompt_personality}" + + return prompt_initiative_select,nodes_for_select,prompt_regular + + def _build_initiative_prompt_check(self,selected_node,prompt_regular): + memory=random.sample(selected_node['memory_items'],3) + prompt_for_check=f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是\"{selected_node['concept']}\"，关于这个话题的记忆有\n{'\n'.join(memory)}\n，以这个作为主题发言合适吗？\ + 请在把握群里的聊天内容的基础上，综合群内的氛围，如果认为应该发言请输出yes，否则输出no，请注意是决定是否需要发言，而不是编写回复内容，除了yes和no不要输出任何回复内容。" + return prompt_for_check,memory + + def _build_initiative_prompt(self,selected_node,prompt_regular,memory): + prompt_for_initiative=f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是\"{selected_node['concept']}\"，关于这个话题的记忆有\n{'\n'.join(memory)}\n，\ + 请在把握群里的聊天内容的基础上，综合群内的氛围，以日常且口语化的口吻，简短且随意一点进行发言，不要说的太有条理，可以有个性。记住不要输出多余内容(包括前后缀，冒号和引号，\ + 括号，表情等)" + return prompt_for_initiative + def get_prompt_info(self,message:str,threshold:float): related_info = '' diff --git a/src/plugins/chat/topic_identifier.py b/src/plugins/chat/topic_identifier.py index b0500c435..0c065896f 100644 --- a/src/plugins/chat/topic_identifier.py +++ b/src/plugins/chat/topic_identifier.py @@ -6,38 +6,42 @@ from nonebot import get_driver from .config import global_config driver = get_driver() -config = driver.config +config = driver.config + class TopicIdentifier: def __init__(self): self.client = OpenAI( - api_key=config.siliconflow_key, - base_url=config.siliconflow_base_url + api_key=config.siliconflow_key, base_url=config.siliconflow_base_url ) - + def identify_topic_llm(self, text: str) -> Optional[str]: """识别消息主题""" - prompt = f"""判断这条消息的主题，如果没有明显主题请回复"无主题"，要求： -1. 主题通常2-4个字，必须简短，要求精准概括，不要太具体。 -2. 建议给出多个主题，之间用英文逗号分割。只输出主题本身就好，不要有前后缀。 - -消息内容：{text}""" + prompt = f"""判断这条消息的主题，如果没有明显主题请回复"无主题"，要求：\ + 1. 主题通常2-4个字，必须简短，要求精准概括，不要太具体。\ + 2. 建议给出多个主题，之间用英文逗号分割。只输出主题本身就好，不要有前后缀。\ + 3. 这里是 + 消息内容：{text}""" response = self.client.chat.completions.create( model=global_config.SILICONFLOW_MODEL_V3, messages=[{"role": "user", "content": prompt}], temperature=0.8, - max_tokens=10 + max_tokens=10, ) - + if not response or not response.choices: print(f"\033[1;31m[错误]\033[0m OpenAI API 返回为空") return None - + # 从 OpenAI API 响应中获取第一个选项的消息内容,并去除首尾空白字符 - topic = response.choices[0].message.content.strip() if response.choices[0].message.content else None - + topic = ( + response.choices[0].message.content.strip() + if response.choices[0].message.content + else None + ) + if topic == "无主题": return None else: @@ -45,7 +49,6 @@ class TopicIdentifier: split_topic = self.parse_topic(topic) return split_topic - def parse_topic(self, topic: str) -> List[str]: """解析主题，返回主题列表""" if not topic or topic == "无主题": @@ -57,44 +60,225 @@ class TopicIdentifier: words = jieba.lcut(text) # 去除停用词和标点符号 stop_words = { - '的', '了', '和', '是', '就', '都', '而', '及', '与', '这', '那', '但', '然', '却', - '因为', '所以', '如果', '虽然', '一个', '我', '你', '他', '她', '它', '我们', '你们', - '他们', '在', '有', '个', '把', '被', '让', '给', '从', '向', '到', '又', '也', '很', - '啊', '吧', '呢', '吗', '呀', '哦', '哈', '么', '嘛', '啦', '哎', '唉', '哇', '嗯', - '哼', '哪', '什么', '怎么', '为什么', '怎样', '如何', '什么样', '这样', '那样', '这么', - '那么', '多少', '几', '谁', '哪里', '哪儿', '什么时候', '何时', '为何', '怎么办', - '怎么样', '这些', '那些', '一些', '一点', '一下', '一直', '一定', '一般', '一样', - '一会儿', '一边', '一起', + "的", + "了", + "和", + "是", + "就", + "都", + "而", + "及", + "与", + "这", + "那", + "但", + "然", + "却", + "因为", + "所以", + "如果", + "虽然", + "一个", + "我", + "你", + "他", + "她", + "它", + "我们", + "你们", + "他们", + "在", + "有", + "个", + "把", + "被", + "让", + "给", + "从", + "向", + "到", + "又", + "也", + "很", + "啊", + "吧", + "呢", + "吗", + "呀", + "哦", + "哈", + "么", + "嘛", + "啦", + "哎", + "唉", + "哇", + "嗯", + "哼", + "哪", + "什么", + "怎么", + "为什么", + "怎样", + "如何", + "什么样", + "这样", + "那样", + "这么", + "那么", + "多少", + "几", + "谁", + "哪里", + "哪儿", + "什么时候", + "何时", + "为何", + "怎么办", + "怎么样", + "这些", + "那些", + "一些", + "一点", + "一下", + "一直", + "一定", + "一般", + "一样", + "一会儿", + "一边", + "一起", # 添加更多量词 - '个', '只', '条', '张', '片', '块', '本', '册', '页', '幅', '面', '篇', '份', - '朵', '颗', '粒', '座', '幢', '栋', '间', '层', '家', '户', '位', '名', '群', - '双', '对', '打', '副', '套', '批', '组', '串', '包', '箱', '袋', '瓶', '罐', + "个", + "只", + "条", + "张", + "片", + "块", + "本", + "册", + "页", + "幅", + "面", + "篇", + "份", + "朵", + "颗", + "粒", + "座", + "幢", + "栋", + "间", + "层", + "家", + "户", + "位", + "名", + "群", + "双", + "对", + "打", + "副", + "套", + "批", + "组", + "串", + "包", + "箱", + "袋", + "瓶", + "罐", # 添加更多介词 - '按', '按照', '把', '被', '比', '比如', '除', '除了', '当', '对', '对于', - '根据', '关于', '跟', '和', '将', '经', '经过', '靠', '连', '论', '通过', - '同', '往', '为', '为了', '围绕', '于', '由', '由于', '与', '在', '沿', '沿着', - '依', '依照', '以', '因', '因为', '用', '由', '与', '自', '自从' + "按", + "按照", + "把", + "被", + "比", + "比如", + "除", + "除了", + "当", + "对", + "对于", + "根据", + "关于", + "跟", + "和", + "将", + "经", + "经过", + "靠", + "连", + "论", + "通过", + "同", + "往", + "为", + "为了", + "围绕", + "于", + "由", + "由于", + "与", + "在", + "沿", + "沿着", + "依", + "依照", + "以", + "因", + "因为", + "用", + "由", + "与", + "自", + "自从", } - + # 过滤掉停用词和标点符号,只保留名词和动词 filtered_words = [] for word in words: if word not in stop_words and not word.strip() in { - '。', '，', '、', '：', '；', '！', '？', '"', '"', ''', ''', - '（', '）', '【', '】', '《', '》', '…', '—', '·', '、', '~', - '～', '+', '=', '-','[',']' + "。", + "，", + "、", + "：", + "；", + "！", + "？", + '"', + '"', + """, """, + "（", + "）", + "【", + "】", + "《", + "》", + "…", + "—", + "·", + "、", + "~", + "～", + "+", + "=", + "-", + "[", + "]", }: filtered_words.append(word) - + # 统计词频 word_freq = {} for word in filtered_words: word_freq[word] = word_freq.get(word, 0) + 1 - + # 按词频排序，取前3个 sorted_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True) top_words = [word for word, freq in sorted_words[:3]] - + return top_words if top_words else None -topic_identifier = TopicIdentifier() \ No newline at end of file + +topic_identifier = TopicIdentifier()