ea7c1f22f9
- 在 RelationshipFetcher 中添加 build_chat_stream_impression 方法，支持聊天流印象信息构建 - 扩展数据库模型，为 ChatStreams 表添加聊天流印象相关字段（stream_impression_text、stream_chat_style、stream_topic_keywords、stream_interest_score） - 为 UserRelationships 表添加用户别名和偏好关键词字段（user_aliases、preference_keywords） - 在 DefaultReplyer、Prompt 和 S4U PromptBuilder 中集成用户关系信息和聊天流印象的组合输出 - 重构工具系统，为 BaseTool 添加 chat_stream 参数支持上下文感知 - 移除旧的 ChatterRelationshipTracker 及相关关系追踪逻辑，统一使用评分API - 在 AffinityChatterPlugin 中添加 UserProfileTool 和 ChatStreamImpressionTool 支持 - 优化计划执行器，移除关系追踪相关代码并改进错误处理 BREAKING CHANGE: 移除了 ChatterRelationshipTracker 类及相关的关系追踪功能，现在统一使用 scoring_api 进行关系管理。BaseTool 构造函数现在需要 chat_stream 参数。
444 lines
19 KiB
Python
444 lines
19 KiB
Python
import asyncio
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# 旧的Hippocampus系统已被移除，现在使用增强记忆系统
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# from src.chat.memory_system.enhanced_memory_manager import enhanced_memory_manager
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import random
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import time
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from datetime import datetime
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from src.chat.express.expression_selector import expression_selector
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from src.chat.message_receive.chat_stream import ChatStream
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from src.chat.message_receive.message import MessageRecvS4U
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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.utils.prompt import Prompt, global_prompt_manager
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from src.chat.utils.utils import get_recent_group_speaker
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from src.common.logger import get_logger
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from src.config.config import global_config
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from src.mais4u.mais4u_chat.internal_manager import internal_manager
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from src.mais4u.mais4u_chat.screen_manager import screen_manager
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from src.mais4u.mais4u_chat.super_chat_manager import get_super_chat_manager
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from src.mais4u.s4u_config import s4u_config
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from src.person_info.person_info import PersonInfoManager, get_person_info_manager
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from src.person_info.relationship_fetcher import relationship_fetcher_manager
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from .s4u_mood_manager import mood_manager
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logger = get_logger("prompt")
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def init_prompt():
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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("\n关于你们的关系，你需要知道：\n{relation_info}\n", "relation_prompt")
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Prompt("你回想起了一些事情：\n{memory_info}\n", "memory_prompt")
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Prompt(
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"""
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你的名字是麦麦, 是千石可乐开发的程序，可以在QQ，微信等平台发言，你现在正在哔哩哔哩作为虚拟主播进行直播
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虽然是个AI，但你在努力扮演真人, 人设的配色是橙色和紫色和绿色
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你有一头法式橙色卷发，你很可爱，穿戴英式侦探风格学院风裙子和帽子，你正在一个教室场景里进行虚拟线上直播。不过不要刻意提及这些内容，自然回复
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你可以看见用户发送的弹幕，礼物和superchat
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{screen_info}
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{internal_state}
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{relation_info_block}
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{memory_block}
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{expression_habits_block}
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你现在的主要任务是和 {sender_name} 发送的弹幕聊天。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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{sc_info}
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{background_dialogue_prompt}
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--------------------------------
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{time_block}
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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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{gift_info}
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回复简短一些，平淡一些，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。
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表现的有个性，不要随意服从他人要求，积极互动。你现在的心情是：{mood_state}
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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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Prompt(
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"""
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你的名字是麦麦, 是千石可乐开发的程序，可以在QQ，微信等平台发言，你现在正在哔哩哔哩作为虚拟主播进行直播
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虽然是个AI，但你在努力扮演真人, 人设的配色是橙色和紫色和绿色
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你有一头法式橙色卷发，你很可爱，穿戴英式侦探风格学院风裙子和帽子，你正在一个教室场景里进行虚拟线上直播。不过不要刻意提及这些内容，自然回复
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你可以看见用户发送的弹幕，礼物和superchat
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你可以看见面前的屏幕，目前屏幕的内容是:
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{screen_info}
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{memory_block}
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{expression_habits_block}
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{sc_info}
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{time_block}
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{chat_info_danmu}
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--------------------------------
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以上是你和弹幕的对话，与此同时，你在与QQ群友聊天，聊天记录如下：
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{chat_info_qq}
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--------------------------------
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你刚刚回复了QQ群，你内心的想法是：{mind}
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请根据你内心的想法，组织一条回复，在直播间进行发言，可以点名吐槽对象，让观众知道你在说谁
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{gift_info}
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回复简短一些，平淡一些，可以参考贴吧，知乎和微博的回复风格。不要浮夸，有逻辑和条理。
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表现的有个性，不要随意服从他人要求，积极互动。你现在的心情是：{mood_state}
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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 )。
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你的发言：
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""",
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"s4u_prompt_internal", # 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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@staticmethod
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async def build_expression_habits(chat_stream: ChatStream, chat_history, target):
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style_habits = []
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grammar_habits = []
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# 使用统一的表达方式选择入口（支持classic和exp_model模式）
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selected_expressions = await expression_selector.select_suitable_expressions(
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chat_id=chat_stream.stream_id,
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chat_history=chat_history,
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target_message=target,
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max_num=12,
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min_num=5
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)
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if selected_expressions:
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logger.debug(f" 使用处理器选中的{len(selected_expressions)}个表达方式")
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for expr in selected_expressions:
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if isinstance(expr, dict) and "situation" in expr and "style" in expr:
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expr_type = expr.get("type", "style")
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if expr_type == "grammar":
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grammar_habits.append(f"当{expr['situation']}时，使用 {expr['style']}")
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else:
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style_habits.append(f"当{expr['situation']}时，使用 {expr['style']}")
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else:
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logger.debug("没有从处理器获得表达方式，将使用空的表达方式")
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# 不再在replyer中进行随机选择，全部交给处理器处理
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style_habits_str = "\n".join(style_habits)
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grammar_habits_str = "\n".join(grammar_habits)
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# 动态构建expression habits块
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expression_habits_block = ""
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if style_habits_str.strip():
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expression_habits_block += f"你可以参考以下的语言习惯，如果情景合适就使用，不要盲目使用,不要生硬使用，而是结合到表达中：\n{style_habits_str}\n\n"
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if grammar_habits_str.strip():
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expression_habits_block += f"请你根据情景使用以下句法：\n{grammar_habits_str}\n"
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return expression_habits_block
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@staticmethod
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async def build_relation_info(chat_stream) -> str:
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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.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.affinity_flow.enable_relationship_tracking and who_chat_in_group:
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relationship_fetcher = relationship_fetcher_manager.get_fetcher(chat_stream.stream_id)
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# 将 (platform, user_id, nickname) 转换为 person_id
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person_ids = []
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for person in who_chat_in_group:
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person_id = PersonInfoManager.get_person_id(person[0], person[1])
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person_ids.append(person_id)
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# 构建用户关系信息和聊天流印象信息
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user_relation_tasks = [relationship_fetcher.build_relation_info(person_id, points_num=3) for person_id in person_ids]
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stream_impression_task = relationship_fetcher.build_chat_stream_impression(chat_stream.stream_id)
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# 并行获取所有信息
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results = await asyncio.gather(*user_relation_tasks, stream_impression_task)
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relation_info_list = results[:-1] # 用户关系信息
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stream_impression = results[-1] # 聊天流印象
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# 组合用户关系信息和聊天流印象
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combined_info_parts = []
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if user_relation_info := "".join(relation_info_list):
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combined_info_parts.append(user_relation_info)
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if stream_impression:
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combined_info_parts.append(stream_impression)
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if combined_info := "\n\n".join(combined_info_parts):
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relation_prompt = await global_prompt_manager.format_prompt(
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"relation_prompt", relation_info=combined_info
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)
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return relation_prompt
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@staticmethod
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async def build_memory_block(text: str) -> str:
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# 使用新的统一记忆系统检索记忆
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try:
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from src.chat.memory_system import get_memory_system
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memory_system = get_memory_system()
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enhanced_memories = await memory_system.retrieve_relevant_memories(
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query_text=text,
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user_id="system", # 系统查询
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scope_id="system",
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limit=5,
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)
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related_memory_info = ""
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if enhanced_memories:
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for memory_chunk in enhanced_memories:
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related_memory_info += memory_chunk.display or memory_chunk.text_content or ""
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return await global_prompt_manager.format_prompt(
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"memory_prompt", memory_info=related_memory_info.strip()
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)
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return ""
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except Exception as e:
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logger.warning(f"增强记忆系统检索失败: {e}")
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return ""
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@staticmethod
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async def build_chat_history_prompts(chat_stream: ChatStream, message: MessageRecvS4U):
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message_list_before_now = await 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=300,
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)
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talk_type = f"{message.message_info.platform}:{message.chat_stream.user_info.user_id!s}"
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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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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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core_dialogue_list.append(msg_dict)
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elif msg_dict.get("reply_to") and talk_type != msg_dict.get("reply_to"):
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background_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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background_dialogue_prompt = ""
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if background_dialogue_list:
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context_msgs = background_dialogue_list[-s4u_config.max_context_message_length :]
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background_dialogue_prompt_str = await build_readable_messages(
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context_msgs,
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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_str}"
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core_msg_str = ""
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if core_dialogue_list:
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core_dialogue_list = core_dialogue_list[-s4u_config.max_core_message_length :]
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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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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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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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for msg in all_msg_seg_list:
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core_msg_str += msg
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all_dialogue_prompt = await 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=20,
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)
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all_dialogue_prompt_str = await build_readable_messages(
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all_dialogue_prompt,
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timestamp_mode="normal_no_YMD",
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show_pic=False,
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)
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return core_msg_str, background_dialogue_prompt, all_dialogue_prompt_str
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@staticmethod
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def build_gift_info(message: MessageRecvS4U):
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if message.is_gift:
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return f"这是一条礼物信息，{message.gift_name} x{message.gift_count}，请注意这位用户"
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else:
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if message.is_fake_gift:
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return f"{message.processed_plain_text}（注意：这是一条普通弹幕信息，对方没有真的发送礼物，不是礼物信息，注意区分，如果对方在发假的礼物骗你，请反击）"
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return ""
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@staticmethod
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def build_sc_info(message: MessageRecvS4U):
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super_chat_manager = get_super_chat_manager()
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return super_chat_manager.build_superchat_summary_string(message.chat_stream.stream_id)
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async def build_prompt_normal(
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self,
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message: MessageRecvS4U,
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message_txt: str,
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) -> str:
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chat_stream = message.chat_stream
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person_id = PersonInfoManager.get_person_id(
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message.chat_stream.user_info.platform, message.chat_stream.user_info.user_id
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)
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person_info_manager = get_person_info_manager()
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person_name = await person_info_manager.get_value(person_id, "person_name")
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if message.chat_stream.user_info.user_nickname:
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if person_name:
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sender_name = f"[{message.chat_stream.user_info.user_nickname}]（你叫ta{person_name}）"
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else:
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sender_name = f"[{message.chat_stream.user_info.user_nickname}]"
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else:
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sender_name = f"用户({message.chat_stream.user_info.user_id})"
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relation_info_block, memory_block, expression_habits_block = await asyncio.gather(
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self.build_relation_info(chat_stream),
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self.build_memory_block(message_txt),
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self.build_expression_habits(chat_stream, message_txt, sender_name),
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)
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core_dialogue_prompt, background_dialogue_prompt, all_dialogue_prompt = await self.build_chat_history_prompts(
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chat_stream, message
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)
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gift_info = self.build_gift_info(message)
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sc_info = self.build_sc_info(message)
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screen_info = screen_manager.get_screen_str()
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internal_state = internal_manager.get_internal_state_str()
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time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
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mood = mood_manager.get_mood_by_chat_id(chat_stream.stream_id)
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template_name = "s4u_prompt"
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if not message.is_internal:
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prompt = await global_prompt_manager.format_prompt(
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template_name,
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time_block=time_block,
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expression_habits_block=expression_habits_block,
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relation_info_block=relation_info_block,
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memory_block=memory_block,
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screen_info=screen_info,
|
||
internal_state=internal_state,
|
||
gift_info=gift_info,
|
||
sc_info=sc_info,
|
||
sender_name=sender_name,
|
||
core_dialogue_prompt=core_dialogue_prompt,
|
||
background_dialogue_prompt=background_dialogue_prompt,
|
||
message_txt=message_txt,
|
||
mood_state=mood.mood_state,
|
||
)
|
||
else:
|
||
prompt = await global_prompt_manager.format_prompt(
|
||
"s4u_prompt_internal",
|
||
time_block=time_block,
|
||
expression_habits_block=expression_habits_block,
|
||
relation_info_block=relation_info_block,
|
||
memory_block=memory_block,
|
||
screen_info=screen_info,
|
||
gift_info=gift_info,
|
||
sc_info=sc_info,
|
||
chat_info_danmu=all_dialogue_prompt,
|
||
chat_info_qq=message.chat_info,
|
||
mind=message.processed_plain_text,
|
||
mood_state=mood.mood_state,
|
||
)
|
||
|
||
# print(prompt)
|
||
|
||
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, strict=False))
|
||
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()
|