feat：一对多的新模式

src/mais4u/mais4u_chat/s4u_prompt.py

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+
+from src.config.config import global_config
+from src.common.logger import get_logger
+from src.individuality.individuality import get_individuality
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
+from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
+from src.chat.message_receive.message import MessageRecv
+import time
+from src.chat.utils.utils import get_recent_group_speaker
+from src.chat.memory_system.Hippocampus import hippocampus_manager
+import random
+
+from src.person_info.relationship_manager import get_relationship_manager
+
+logger = get_logger("prompt")
+
+
+def init_prompt():
+    Prompt("你正在qq群里聊天，下面是群里在聊的内容：", "chat_target_group1")
+    Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
+    Prompt("在群里聊天", "chat_target_group2")
+    Prompt("和{sender_name}私聊", "chat_target_private2")
+
+    Prompt("\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n", "knowledge_prompt")
+
+
+    Prompt(
+        """
+你的名字叫{bot_name}，昵称是：{bot_other_names}，{prompt_personality}。
+你现在的主要任务是和 {sender_name} 聊天。同时，也有其他用户会参与你们的聊天，但是你主要还是关注你和{sender_name}的聊天内容。
+
+{background_dialogue_prompt}
+--------------------------------
+{now_time}
+这是你和{sender_name}的对话，你们正在交流中：
+{core_dialogue_prompt}
+
+{message_txt}
+回复可以简短一些。可以参考贴吧，知乎和微博的回复风格，回复不要浮夸，不要用夸张修辞，平淡一些。
+不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容，现在{sender_name}正在等待你的回复。
+你的回复风格不要浮夸，有逻辑和条理，请你继续回复{sender_name}。""",
+        "s4u_prompt",  # New template for private CHAT chat
+    )
+
+
+class PromptBuilder:
+    def __init__(self):
+        self.prompt_built = ""
+        self.activate_messages = ""
+
+    async def build_prompt_normal(
+        self,
+        message,
+        chat_stream,
+        message_txt: str,
+        sender_name: str = "某人",
+    ) -> str:
+        prompt_personality = get_individuality().get_prompt(x_person=2, level=2)
+        is_group_chat = bool(chat_stream.group_info)
+
+        who_chat_in_group = []
+        if is_group_chat:
+            who_chat_in_group = get_recent_group_speaker(
+                chat_stream.stream_id,
+                (chat_stream.user_info.platform, chat_stream.user_info.user_id) if chat_stream.user_info else None,
+                limit=global_config.normal_chat.max_context_size,
+            )
+        elif chat_stream.user_info:
+            who_chat_in_group.append(
+                (chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname)
+            )
+
+        relation_prompt = ""
+        if global_config.relationship.enable_relationship:
+            for person in who_chat_in_group:
+                relationship_manager = get_relationship_manager()
+                relation_prompt += await relationship_manager.build_relationship_info(person)
+
+
+        memory_prompt = ""
+        related_memory = await hippocampus_manager.get_memory_from_text(
+            text=message_txt, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
+        )
+
+        related_memory_info = ""
+        if related_memory:
+            for memory in related_memory:
+                related_memory_info += memory[1]
+            memory_prompt = await global_prompt_manager.format_prompt(
+                "memory_prompt", related_memory_info=related_memory_info
+            )
+
+        message_list_before_now = get_raw_msg_before_timestamp_with_chat(
+            chat_id=chat_stream.stream_id,
+            timestamp=time.time(),
+            limit=100,
+        )
+        
+
+        # 分别筛选核心对话和背景对话
+        core_dialogue_list = []
+        background_dialogue_list = []
+        bot_id = str(global_config.bot.qq_account)
+        target_user_id = str(message.chat_stream.user_info.user_id)
+
+        for msg_dict in message_list_before_now:
+            try:
+                # 直接通过字典访问
+                msg_user_id = str(msg_dict.get('user_id'))
+                
+                if msg_user_id == bot_id or msg_user_id == target_user_id:
+                    core_dialogue_list.append(msg_dict)
+                else:
+                    background_dialogue_list.append(msg_dict)
+            except Exception as e:
+                logger.error(f"无法处理历史消息记录: {msg_dict}, 错误: {e}")
+                
+        if background_dialogue_list:
+            latest_25_msgs = background_dialogue_list[-25:]
+            background_dialogue_prompt = build_readable_messages(
+                latest_25_msgs,
+                merge_messages=True,
+                timestamp_mode = "normal_no_YMD",
+                show_pic = False,
+            )
+            background_dialogue_prompt = f"这是其他用户的发言：\n{background_dialogue_prompt}"
+        else:
+            background_dialogue_prompt = ""
+                
+        # 分别获取最新50条和最新25条（从message_list_before_now截取）
+        core_dialogue_list = core_dialogue_list[-50:]
+        
+        first_msg = core_dialogue_list[0]
+        start_speaking_user_id = first_msg.get('user_id')
+        if start_speaking_user_id == bot_id:
+            last_speaking_user_id = bot_id
+            msg_seg_str = "你的发言：\n"
+        else:
+            start_speaking_user_id = target_user_id
+            last_speaking_user_id = start_speaking_user_id
+            msg_seg_str = "对方的发言：\n"
+        
+        msg_seg_str += f"{first_msg.get('processed_plain_text')}\n"
+
+        all_msg_seg_list = []
+        for msg in core_dialogue_list[1:]:
+            speaker = msg.get('user_id')
+            if speaker == last_speaking_user_id:
+                #还是同一个人讲话
+                msg_seg_str += f"{msg.get('processed_plain_text')}\n"
+            else:
+                #换人了
+                msg_seg_str = f"{msg_seg_str}\n"
+                all_msg_seg_list.append(msg_seg_str)
+                
+                if speaker == bot_id:
+                    msg_seg_str = "你的发言：\n"
+                else:
+                    msg_seg_str = "对方的发言：\n"
+                
+                msg_seg_str += f"{msg.get('processed_plain_text')}\n"
+                last_speaking_user_id = speaker
+            
+        all_msg_seg_list.append(msg_seg_str)
+
+
+        core_msg_str = ""
+        for msg in all_msg_seg_list:
+            # print(f"msg: {msg}")
+            core_msg_str += msg
+
+        now_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+        now_time = f"现在的时间是：{now_time}"
+
+        template_name = "s4u_prompt"
+        effective_sender_name = sender_name
+
+        prompt = await global_prompt_manager.format_prompt(
+            template_name,
+            relation_prompt=relation_prompt,
+            sender_name=effective_sender_name,
+            memory_prompt=memory_prompt,
+            core_dialogue_prompt=core_msg_str,
+            background_dialogue_prompt=background_dialogue_prompt,
+            message_txt=message_txt,
+            bot_name=global_config.bot.nickname,
+            bot_other_names="/".join(global_config.bot.alias_names),
+            prompt_personality=prompt_personality,
+            now_time=now_time,
+        )
+
+        return prompt
+
+
+def weighted_sample_no_replacement(items, weights, k) -> list:
+    """
+    加权且不放回地随机抽取k个元素。
+
+    参数：
+        items: 待抽取的元素列表
+        weights: 每个元素对应的权重（与items等长，且为正数）
+        k: 需要抽取的元素个数
+    返回：
+        selected: 按权重加权且不重复抽取的k个元素组成的列表
+
+        如果 items 中的元素不足 k 个，就只会返回所有可用的元素
+
+    实现思路：
+        每次从当前池中按权重加权随机选出一个元素，选中后将其从池中移除，重复k次。
+        这样保证了：
+        1. count越大被选中概率越高
+        2. 不会重复选中同一个元素
+    """
+    selected = []
+    pool = list(zip(items, weights))
+    for _ in range(min(k, len(pool))):
+        total = sum(w for _, w in pool)
+        r = random.uniform(0, total)
+        upto = 0
+        for idx, (item, weight) in enumerate(pool):
+            upto += weight
+            if upto >= r:
+                selected.append(item)
+                pool.pop(idx)
+                break
+    return selected
+
+
+init_prompt()
+prompt_builder = PromptBuilder()