244 lines
9.4 KiB
Python
244 lines
9.4 KiB
Python
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from src.config.config import global_config
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from src.common.logger import get_logger
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from src.individuality.individuality import get_individuality
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
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from src.chat.message_receive.message import MessageRecv
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import time
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from src.chat.utils.utils import get_recent_group_speaker
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from src.chat.memory_system.Hippocampus import hippocampus_manager
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import random
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from src.person_info.relationship_manager import get_relationship_manager
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logger = get_logger("prompt")
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def init_prompt():
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Prompt("你正在qq群里聊天，下面是群里在聊的内容：", "chat_target_group1")
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Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
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Prompt("在群里聊天", "chat_target_group2")
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Prompt("和{sender_name}私聊", "chat_target_private2")
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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")
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Prompt(
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"""
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你的名字叫{bot_name}，昵称是：{bot_other_names}，{prompt_personality}。
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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，你可以参考他们的回复内容，但是你主要还是关注你和{sender_name}的聊天内容。
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{background_dialogue_prompt}
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--------------------------------
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{now_time}
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这是你和{sender_name}的对话，你们正在交流中：
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{core_dialogue_prompt}
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对方最新发送的内容：{message_txt}
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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。
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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。
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你的回复风格不要浮夸，有逻辑和条理，请你继续回复{sender_name}。
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你的发言：
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""",
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"s4u_prompt", # New template for private CHAT chat
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)
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class PromptBuilder:
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def __init__(self):
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self.prompt_built = ""
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self.activate_messages = ""
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async def build_prompt_normal(
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self,
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message,
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chat_stream,
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message_txt: str,
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sender_name: str = "某人",
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) -> str:
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prompt_personality = get_individuality().get_prompt(x_person=2, level=2)
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is_group_chat = bool(chat_stream.group_info)
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who_chat_in_group = []
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if is_group_chat:
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who_chat_in_group = get_recent_group_speaker(
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chat_stream.stream_id,
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(chat_stream.user_info.platform, chat_stream.user_info.user_id) if chat_stream.user_info else None,
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limit=global_config.normal_chat.max_context_size,
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)
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elif chat_stream.user_info:
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who_chat_in_group.append(
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(chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname)
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)
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relation_prompt = ""
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if global_config.relationship.enable_relationship:
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for person in who_chat_in_group:
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relationship_manager = get_relationship_manager()
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relation_prompt += await relationship_manager.build_relationship_info(person)
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memory_prompt = ""
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related_memory = await hippocampus_manager.get_memory_from_text(
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text=message_txt, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
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)
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related_memory_info = ""
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if related_memory:
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for memory in related_memory:
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related_memory_info += memory[1]
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memory_prompt = await global_prompt_manager.format_prompt(
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"memory_prompt", related_memory_info=related_memory_info
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)
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message_list_before_now = get_raw_msg_before_timestamp_with_chat(
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chat_id=chat_stream.stream_id,
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timestamp=time.time(),
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limit=100,
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)
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talk_type = message.message_info.platform + ":" + message.chat_stream.user_info.user_id
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print(f"talk_type: {talk_type}")
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# 分别筛选核心对话和背景对话
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core_dialogue_list = []
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background_dialogue_list = []
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bot_id = str(global_config.bot.qq_account)
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target_user_id = str(message.chat_stream.user_info.user_id)
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for msg_dict in message_list_before_now:
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try:
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# 直接通过字典访问
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msg_user_id = str(msg_dict.get('user_id'))
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if msg_user_id == bot_id:
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if msg_dict.get("reply_to") and talk_type == msg_dict.get("reply_to"):
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print(f"reply: {msg_dict.get('reply_to')}")
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core_dialogue_list.append(msg_dict)
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else:
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background_dialogue_list.append(msg_dict)
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elif msg_user_id == target_user_id:
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core_dialogue_list.append(msg_dict)
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else:
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background_dialogue_list.append(msg_dict)
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except Exception as e:
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logger.error(f"无法处理历史消息记录: {msg_dict}, 错误: {e}")
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if background_dialogue_list:
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latest_25_msgs = background_dialogue_list[-25:]
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background_dialogue_prompt = build_readable_messages(
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latest_25_msgs,
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merge_messages=True,
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timestamp_mode = "normal_no_YMD",
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show_pic = False,
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)
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background_dialogue_prompt = f"这是其他用户的发言：\n{background_dialogue_prompt}"
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else:
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background_dialogue_prompt = ""
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# 分别获取最新50条和最新25条（从message_list_before_now截取）
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core_dialogue_list = core_dialogue_list[-50:]
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first_msg = core_dialogue_list[0]
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start_speaking_user_id = first_msg.get('user_id')
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if start_speaking_user_id == bot_id:
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last_speaking_user_id = bot_id
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msg_seg_str = "你的发言：\n"
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else:
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start_speaking_user_id = target_user_id
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last_speaking_user_id = start_speaking_user_id
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msg_seg_str = "对方的发言：\n"
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msg_seg_str += f"{time.strftime('%H:%M:%S', time.localtime(first_msg.get('time')))}: {first_msg.get('processed_plain_text')}\n"
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all_msg_seg_list = []
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for msg in core_dialogue_list[1:]:
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speaker = msg.get('user_id')
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if speaker == last_speaking_user_id:
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#还是同一个人讲话
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msg_seg_str += f"{time.strftime('%H:%M:%S', time.localtime(msg.get('time')))}: {msg.get('processed_plain_text')}\n"
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else:
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#换人了
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msg_seg_str = f"{msg_seg_str}\n"
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all_msg_seg_list.append(msg_seg_str)
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if speaker == bot_id:
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msg_seg_str = "你的发言：\n"
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else:
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msg_seg_str = "对方的发言：\n"
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msg_seg_str += f"{time.strftime('%H:%M:%S', time.localtime(msg.get('time')))}: {msg.get('processed_plain_text')}\n"
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last_speaking_user_id = speaker
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all_msg_seg_list.append(msg_seg_str)
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core_msg_str = ""
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for msg in all_msg_seg_list:
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# print(f"msg: {msg}")
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core_msg_str += msg
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now_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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now_time = f"现在的时间是：{now_time}"
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template_name = "s4u_prompt"
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effective_sender_name = sender_name
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prompt = await global_prompt_manager.format_prompt(
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template_name,
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relation_prompt=relation_prompt,
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sender_name=effective_sender_name,
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memory_prompt=memory_prompt,
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core_dialogue_prompt=core_msg_str,
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background_dialogue_prompt=background_dialogue_prompt,
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message_txt=message_txt,
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bot_name=global_config.bot.nickname,
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bot_other_names="/".join(global_config.bot.alias_names),
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prompt_personality=prompt_personality,
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now_time=now_time,
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)
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return prompt
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def weighted_sample_no_replacement(items, weights, k) -> list:
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"""
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加权且不放回地随机抽取k个元素。
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参数：
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items: 待抽取的元素列表
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weights: 每个元素对应的权重（与items等长，且为正数）
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k: 需要抽取的元素个数
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返回：
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selected: 按权重加权且不重复抽取的k个元素组成的列表
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如果 items 中的元素不足 k 个，就只会返回所有可用的元素
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实现思路：
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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。
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这样保证了：
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1. count越大被选中概率越高
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2. 不会重复选中同一个元素
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"""
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selected = []
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pool = list(zip(items, weights))
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for _ in range(min(k, len(pool))):
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total = sum(w for _, w in pool)
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r = random.uniform(0, total)
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upto = 0
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for idx, (item, weight) in enumerate(pool):
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upto += weight
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if upto >= r:
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selected.append(item)
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pool.pop(idx)
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break
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return selected
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init_prompt()
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prompt_builder = PromptBuilder()
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