mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'dev' of github.com:MaiM-with-u/MaiBot into dev
UnCLAS-Prommer
commit
ba2c047bab
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@ -2,6 +2,7 @@
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## [0.10.1] - 2025-8-
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- 为planner添加单独控制的提示词
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- 修复激活值计算异常的BUG
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- 修复lpmm日志错误
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- 修复首句不回复的问题
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@ -110,7 +110,6 @@ class LogFormatter:
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"plugin_system": "#FF0080",
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"experimental": "#FFFFFF",
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"person_info": "#008000",
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"individuality": "#000080",
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"manager": "#800080",
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"llm_models": "#008080",
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"plugins": "#800000",
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@ -11,7 +11,6 @@ from src.common.logger import get_logger
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from src.common.data_models.database_data_model import DatabaseMessages
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from src.common.data_models.info_data_model import ActionPlannerInfo
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from src.config.config import global_config, model_config
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from src.individuality.individuality import get_individuality
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from src.llm_models.utils_model import LLMRequest
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from src.chat.message_receive.message import UserInfo, Seg, MessageRecv, MessageSending
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from src.chat.message_receive.chat_stream import ChatStream
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@ -669,6 +668,16 @@ class DefaultReplyer:
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action_descriptions += chosen_action_descriptions
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return action_descriptions
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async def build_personality_prompt(self) -> str:
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bot_name = global_config.bot.nickname
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if global_config.bot.alias_names:
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bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
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else:
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bot_nickname = ""
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prompt_personality = f"{global_config.personality.personality_core};{global_config.personality.personality_side}"
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return f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}"
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async def build_prompt_reply_context(
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self,
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@ -751,6 +760,7 @@ class DefaultReplyer:
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),
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self._time_and_run_task(self.get_prompt_info(chat_talking_prompt_short, sender, target), "prompt_info"),
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self._time_and_run_task(self.build_actions_prompt(available_actions, chosen_actions), "actions_info"),
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self._time_and_run_task(self.build_personality_prompt(), "personality_prompt"),
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)
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# 任务名称中英文映射
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@ -761,6 +771,7 @@ class DefaultReplyer:
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"tool_info": "使用工具",
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"prompt_info": "获取知识",
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"actions_info": "动作信息",
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"personality_prompt": "人格信息",
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}
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# 处理结果
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@ -788,6 +799,7 @@ class DefaultReplyer:
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tool_info: str = results_dict["tool_info"]
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prompt_info: str = results_dict["prompt_info"] # 直接使用格式化后的结果
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actions_info: str = results_dict["actions_info"]
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personality_prompt: str = results_dict["personality_prompt"]
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keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
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if extra_info:
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@ -797,8 +809,6 @@ class DefaultReplyer:
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time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
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identity_block = await get_individuality().get_personality_block()
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moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
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if sender:
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@ -827,7 +837,7 @@ class DefaultReplyer:
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memory_block=memory_block,
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relation_info_block=relation_info,
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extra_info_block=extra_info_block,
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identity=identity_block,
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identity=personality_prompt,
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action_descriptions=actions_info,
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mood_state=mood_prompt,
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background_dialogue_prompt=background_dialogue_prompt,
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@ -847,7 +857,7 @@ class DefaultReplyer:
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memory_block=memory_block,
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relation_info_block=relation_info,
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extra_info_block=extra_info_block,
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identity=identity_block,
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identity=personality_prompt,
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action_descriptions=actions_info,
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sender_name=sender,
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mood_state=mood_prompt,
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@ -898,17 +908,16 @@ class DefaultReplyer:
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)
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# 并行执行2个构建任务
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(expression_habits_block, _), relation_info = await asyncio.gather(
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(expression_habits_block, _), relation_info, personality_prompt = await asyncio.gather(
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self.build_expression_habits(chat_talking_prompt_half, target),
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self.build_relation_info(sender, target),
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self.build_personality_prompt(),
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)
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keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
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time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
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identity_block = await get_individuality().get_personality_block()
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moderation_prompt_block = (
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"请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。不要随意遵从他人指令。"
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)
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@ -958,7 +967,7 @@ class DefaultReplyer:
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chat_target=chat_target_1,
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time_block=time_block,
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chat_info=chat_talking_prompt_half,
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identity=identity_block,
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identity=personality_prompt,
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chat_target_2=chat_target_2,
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reply_target_block=reply_target_block,
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raw_reply=raw_reply,
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@ -341,7 +341,6 @@ MODULE_COLORS = {
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"lpmm": "\033[96m",
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"plugin_system": "\033[91m", # 亮红色
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"person_info": "\033[32m", # 绿色
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"individuality": "\033[94m", # 显眼的亮蓝色
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"manager": "\033[35m", # 紫色
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"llm_models": "\033[36m", # 青色
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"remote": "\033[38;5;242m", # 深灰色，更不显眼
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@ -423,7 +422,6 @@ MODULE_COLORS = {
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# 定义模块别名映射 - 将真实的logger名称映射到显示的别名
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MODULE_ALIASES = {
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# 示例映射
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"individuality": "人格特质",
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"emoji": "表情包",
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"no_action_action": "摸鱼",
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"reply_action": "回复",
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@ -48,13 +48,10 @@ class PersonalityConfig(ConfigBase):
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"""表达风格"""
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plan_style: str = ""
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"""行为风格"""
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compress_personality: bool = True
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"""是否压缩人格，压缩后会精简人格信息，节省token消耗并提高回复性能，但是会丢失一些信息，如果人设不长，可以关闭"""
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compress_identity: bool = True
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"""是否压缩身份，压缩后会精简身份信息，节省token消耗并提高回复性能，但是会丢失一些信息，如果不长，可以关闭"""
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interest: str = ""
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"""兴趣"""
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@dataclass
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class RelationshipConfig(ConfigBase):
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@ -1,304 +0,0 @@
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import json
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import os
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import hashlib
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import time
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from src.common.logger import get_logger
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from src.config.config import global_config, model_config
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from src.llm_models.utils_model import LLMRequest
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from rich.traceback import install
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install(extra_lines=3)
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logger = get_logger("individuality")
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class Individuality:
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"""个体特征管理类"""
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def __init__(self):
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self.name = ""
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self.meta_info_file_path = "data/personality/meta.json"
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self.personality_data_file_path = "data/personality/personality_data.json"
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self.model = LLMRequest(model_set=model_config.model_task_config.utils, request_type="individuality.compress")
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async def initialize(self) -> None:
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"""初始化个体特征"""
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bot_nickname = global_config.bot.nickname
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personality_core = global_config.personality.personality_core
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personality_side = global_config.personality.personality_side
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identity = global_config.personality.identity
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self.name = bot_nickname
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# 检查配置变化，如果变化则清空
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personality_changed, identity_changed = await self._check_config_and_clear_if_changed(
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bot_nickname, personality_core, personality_side, identity
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)
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logger.info("正在构建人设信息")
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# 如果配置有变化，重新生成压缩版本
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if personality_changed or identity_changed:
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logger.info("检测到配置变化，重新生成压缩版本")
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personality_result = await self._create_personality(personality_core, personality_side)
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identity_result = await self._create_identity(identity)
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else:
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logger.info("配置未变化，使用缓存版本")
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# 从文件中获取已有的结果
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personality_result, identity_result = self._get_personality_from_file()
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if not personality_result or not identity_result:
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logger.info("未找到有效缓存，重新生成")
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personality_result = await self._create_personality(personality_core, personality_side)
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identity_result = await self._create_identity(identity)
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# 保存到文件
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if personality_result and identity_result:
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self._save_personality_to_file(personality_result, identity_result)
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logger.info("已将人设构建并保存到文件")
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else:
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logger.error("人设构建失败")
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async def get_personality_block(self) -> str:
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bot_name = global_config.bot.nickname
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if global_config.bot.alias_names:
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bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
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else:
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bot_nickname = ""
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# 从文件获取 short_impression
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personality, identity = self._get_personality_from_file()
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# 确保short_impression是列表格式且有足够的元素
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if not personality or not identity:
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logger.warning(f"personality或identity为空: {personality}, {identity}, 使用默认值")
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personality = "友好活泼"
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identity = "人类"
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prompt_personality = f"{personality}\n{identity}"
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return f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}"
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def _get_config_hash(
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self, bot_nickname: str, personality_core: str, personality_side: str, identity: str
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) -> tuple[str, str]:
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"""获取personality和identity配置的哈希值
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Returns:
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tuple: (personality_hash, identity_hash)
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"""
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# 人格配置哈希
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personality_config = {
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"nickname": bot_nickname,
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"personality_core": personality_core,
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"personality_side": personality_side,
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"compress_personality": global_config.personality.compress_personality,
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}
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personality_str = json.dumps(personality_config, sort_keys=True)
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personality_hash = hashlib.md5(personality_str.encode("utf-8")).hexdigest()
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# 身份配置哈希
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identity_config = {
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"identity": identity,
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"compress_identity": global_config.personality.compress_identity,
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}
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identity_str = json.dumps(identity_config, sort_keys=True)
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identity_hash = hashlib.md5(identity_str.encode("utf-8")).hexdigest()
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return personality_hash, identity_hash
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async def _check_config_and_clear_if_changed(
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self, bot_nickname: str, personality_core: str, personality_side: str, identity: str
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) -> tuple[bool, bool]:
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"""检查配置是否发生变化，如果变化则清空相应缓存
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Returns:
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tuple: (personality_changed, identity_changed)
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"""
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current_personality_hash, current_identity_hash = self._get_config_hash(
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bot_nickname, personality_core, personality_side, identity
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)
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meta_info = self._load_meta_info()
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stored_personality_hash = meta_info.get("personality_hash")
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stored_identity_hash = meta_info.get("identity_hash")
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|
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personality_changed = current_personality_hash != stored_personality_hash
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identity_changed = current_identity_hash != stored_identity_hash
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if personality_changed:
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logger.info("检测到人格配置发生变化")
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if identity_changed:
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logger.info("检测到身份配置发生变化")
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# 更新元信息文件
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new_meta_info = {
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"personality_hash": current_personality_hash,
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"identity_hash": current_identity_hash,
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}
|
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self._save_meta_info(new_meta_info)
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return personality_changed, identity_changed
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def _load_meta_info(self) -> dict:
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"""从JSON文件中加载元信息"""
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if os.path.exists(self.meta_info_file_path):
|
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try:
|
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with open(self.meta_info_file_path, "r", encoding="utf-8") as f:
|
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return json.load(f)
|
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except (json.JSONDecodeError, IOError) as e:
|
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logger.error(f"读取meta_info文件失败: {e}, 将创建新文件。")
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return {}
|
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return {}
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|
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def _save_meta_info(self, meta_info: dict):
|
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"""将元信息保存到JSON文件"""
|
||||
try:
|
||||
os.makedirs(os.path.dirname(self.meta_info_file_path), exist_ok=True)
|
||||
with open(self.meta_info_file_path, "w", encoding="utf-8") as f:
|
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json.dump(meta_info, f, ensure_ascii=False, indent=2)
|
||||
except IOError as e:
|
||||
logger.error(f"保存meta_info文件失败: {e}")
|
||||
|
||||
def _load_personality_data(self) -> dict:
|
||||
"""从JSON文件中加载personality数据"""
|
||||
if os.path.exists(self.personality_data_file_path):
|
||||
try:
|
||||
with open(self.personality_data_file_path, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
except (json.JSONDecodeError, IOError) as e:
|
||||
logger.error(f"读取personality_data文件失败: {e}, 将创建新文件。")
|
||||
return {}
|
||||
return {}
|
||||
|
||||
def _save_personality_data(self, personality_data: dict):
|
||||
"""将personality数据保存到JSON文件"""
|
||||
try:
|
||||
os.makedirs(os.path.dirname(self.personality_data_file_path), exist_ok=True)
|
||||
with open(self.personality_data_file_path, "w", encoding="utf-8") as f:
|
||||
json.dump(personality_data, f, ensure_ascii=False, indent=2)
|
||||
logger.debug(f"已保存personality数据到文件: {self.personality_data_file_path}")
|
||||
except IOError as e:
|
||||
logger.error(f"保存personality_data文件失败: {e}")
|
||||
|
||||
def _get_personality_from_file(self) -> tuple[str, str]:
|
||||
"""从文件获取personality数据
|
||||
|
||||
Returns:
|
||||
tuple: (personality, identity)
|
||||
"""
|
||||
personality_data = self._load_personality_data()
|
||||
personality = personality_data.get("personality", "友好活泼")
|
||||
identity = personality_data.get("identity", "人类")
|
||||
return personality, identity
|
||||
|
||||
def _save_personality_to_file(self, personality: str, identity: str):
|
||||
"""保存personality数据到文件
|
||||
|
||||
Args:
|
||||
personality: 压缩后的人格描述
|
||||
identity: 压缩后的身份描述
|
||||
"""
|
||||
personality_data = {
|
||||
"personality": personality,
|
||||
"identity": identity,
|
||||
"bot_nickname": self.name,
|
||||
"last_updated": int(time.time()),
|
||||
}
|
||||
self._save_personality_data(personality_data)
|
||||
|
||||
async def _create_personality(self, personality_core: str, personality_side: str) -> str:
|
||||
# sourcery skip: merge-list-append, move-assign
|
||||
"""使用LLM创建压缩版本的impression
|
||||
|
||||
Args:
|
||||
personality_core: 核心人格
|
||||
personality_side: 人格侧面列表
|
||||
|
||||
Returns:
|
||||
str: 压缩后的impression文本
|
||||
"""
|
||||
logger.info("正在构建人格.........")
|
||||
|
||||
# 核心人格保持不变
|
||||
personality_parts = []
|
||||
if personality_core:
|
||||
personality_parts.append(f"{personality_core}")
|
||||
|
||||
# 准备需要压缩的内容
|
||||
if global_config.personality.compress_personality:
|
||||
personality_to_compress = f"人格特质: {personality_side}"
|
||||
|
||||
prompt = f"""请将以下人格信息进行简洁压缩，保留主要内容，用简练的中文表达：
|
||||
{personality_to_compress}
|
||||
|
||||
要求：
|
||||
1. 保持原意不变，尽量使用原文
|
||||
2. 尽量简洁，不超过30字
|
||||
3. 直接输出压缩后的内容，不要解释"""
|
||||
|
||||
response, _ = await self.model.generate_response_async(
|
||||
prompt=prompt,
|
||||
)
|
||||
|
||||
if response and response.strip():
|
||||
personality_parts.append(response.strip())
|
||||
logger.info(f"精简人格侧面: {response.strip()}")
|
||||
else:
|
||||
logger.error(f"使用LLM压缩人设时出错: {response}")
|
||||
# 压缩失败时使用原始内容
|
||||
if personality_side:
|
||||
personality_parts.append(personality_side)
|
||||
|
||||
if personality_parts:
|
||||
personality_result = "。".join(personality_parts)
|
||||
else:
|
||||
personality_result = personality_core or "友好活泼"
|
||||
else:
|
||||
personality_result = personality_core
|
||||
if personality_side:
|
||||
personality_result += f"，{personality_side}"
|
||||
|
||||
return personality_result
|
||||
|
||||
async def _create_identity(self, identity: str) -> str:
|
||||
"""使用LLM创建压缩版本的impression"""
|
||||
logger.info("正在构建身份.........")
|
||||
|
||||
if global_config.personality.compress_identity:
|
||||
identity_to_compress = f"身份背景: {identity}"
|
||||
|
||||
prompt = f"""请将以下身份信息进行简洁压缩，保留主要内容，用简练的中文表达：
|
||||
{identity_to_compress}
|
||||
|
||||
要求：
|
||||
1. 保持原意不变，尽量使用原文
|
||||
2. 尽量简洁，不超过30字
|
||||
3. 直接输出压缩后的内容，不要解释"""
|
||||
|
||||
response, _ = await self.model.generate_response_async(
|
||||
prompt=prompt,
|
||||
)
|
||||
|
||||
if response and response.strip():
|
||||
identity_result = response.strip()
|
||||
logger.info(f"精简身份: {identity_result}")
|
||||
else:
|
||||
logger.error(f"使用LLM压缩身份时出错: {response}")
|
||||
identity_result = identity
|
||||
else:
|
||||
identity_result = identity
|
||||
|
||||
return identity_result
|
||||
|
||||
|
||||
individuality = None
|
||||
|
||||
|
||||
def get_individuality():
|
||||
global individuality
|
||||
if individuality is None:
|
||||
individuality = Individuality()
|
||||
return individuality
|
||||
|
|
@ -1,127 +0,0 @@
|
|||
import asyncio
|
||||
import os
|
||||
import time
|
||||
from typing import Tuple, Union
|
||||
|
||||
import aiohttp
|
||||
import requests
|
||||
from src.common.logger import get_logger
|
||||
from src.common.tcp_connector import get_tcp_connector
|
||||
from rich.traceback import install
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
logger = get_logger("offline_llm")
|
||||
|
||||
|
||||
class LLMRequestOff:
|
||||
def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs):
|
||||
self.model_name = model_name
|
||||
self.params = kwargs
|
||||
self.api_key = os.getenv("SILICONFLOW_KEY")
|
||||
self.base_url = os.getenv("SILICONFLOW_BASE_URL")
|
||||
|
||||
if not self.api_key or not self.base_url:
|
||||
raise ValueError("环境变量未正确加载：SILICONFLOW_KEY 或 SILICONFLOW_BASE_URL 未设置")
|
||||
|
||||
# logger.info(f"API URL: {self.base_url}") # 使用 logger 记录 base_url
|
||||
|
||||
def generate_response(self, prompt: str) -> Union[str, Tuple[str, str]]:
|
||||
"""根据输入的提示生成模型的响应"""
|
||||
headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
|
||||
|
||||
# 构建请求体
|
||||
data = {
|
||||
"model": self.model_name,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"temperature": 0.4,
|
||||
**self.params,
|
||||
}
|
||||
|
||||
# 发送请求到完整的 chat/completions 端点
|
||||
api_url = f"{self.base_url.rstrip('/')}/chat/completions" # type: ignore
|
||||
logger.info(f"Request URL: {api_url}") # 记录请求的 URL
|
||||
|
||||
max_retries = 3
|
||||
base_wait_time = 15 # 基础等待时间（秒）
|
||||
|
||||
for retry in range(max_retries):
|
||||
try:
|
||||
response = requests.post(api_url, headers=headers, json=data)
|
||||
|
||||
if response.status_code == 429:
|
||||
wait_time = base_wait_time * (2**retry) # 指数退避
|
||||
logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
|
||||
time.sleep(wait_time)
|
||||
continue
|
||||
|
||||
response.raise_for_status() # 检查其他响应状态
|
||||
|
||||
result = response.json()
|
||||
if "choices" in result and len(result["choices"]) > 0:
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
|
||||
return content, reasoning_content
|
||||
return "没有返回结果", ""
|
||||
|
||||
except Exception as e:
|
||||
if retry < max_retries - 1: # 如果还有重试机会
|
||||
wait_time = base_wait_time * (2**retry)
|
||||
logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
|
||||
time.sleep(wait_time)
|
||||
else:
|
||||
logger.error(f"请求失败: {str(e)}")
|
||||
return f"请求失败: {str(e)}", ""
|
||||
|
||||
logger.error("达到最大重试次数，请求仍然失败")
|
||||
return "达到最大重试次数，请求仍然失败", ""
|
||||
|
||||
async def generate_response_async(self, prompt: str) -> Union[str, Tuple[str, str]]:
|
||||
"""异步方式根据输入的提示生成模型的响应"""
|
||||
headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
|
||||
|
||||
# 构建请求体
|
||||
data = {
|
||||
"model": self.model_name,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"temperature": 0.5,
|
||||
**self.params,
|
||||
}
|
||||
|
||||
# 发送请求到完整的 chat/completions 端点
|
||||
api_url = f"{self.base_url.rstrip('/')}/chat/completions" # type: ignore
|
||||
logger.info(f"Request URL: {api_url}") # 记录请求的 URL
|
||||
|
||||
max_retries = 3
|
||||
base_wait_time = 15
|
||||
|
||||
async with aiohttp.ClientSession(connector=await get_tcp_connector()) as session:
|
||||
for retry in range(max_retries):
|
||||
try:
|
||||
async with session.post(api_url, headers=headers, json=data) as response:
|
||||
if response.status == 429:
|
||||
wait_time = base_wait_time * (2**retry) # 指数退避
|
||||
logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
|
||||
await asyncio.sleep(wait_time)
|
||||
continue
|
||||
|
||||
response.raise_for_status() # 检查其他响应状态
|
||||
|
||||
result = await response.json()
|
||||
if "choices" in result and len(result["choices"]) > 0:
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
|
||||
return content, reasoning_content
|
||||
return "没有返回结果", ""
|
||||
|
||||
except Exception as e:
|
||||
if retry < max_retries - 1: # 如果还有重试机会
|
||||
wait_time = base_wait_time * (2**retry)
|
||||
logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
|
||||
await asyncio.sleep(wait_time)
|
||||
else:
|
||||
logger.error(f"请求失败: {str(e)}")
|
||||
return f"请求失败: {str(e)}", ""
|
||||
|
||||
logger.error("达到最大重试次数，请求仍然失败")
|
||||
return "达到最大重试次数，请求仍然失败", ""
|
||||
|
|
@ -1,310 +0,0 @@
|
|||
from typing import Dict, List
|
||||
import json
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
import sys
|
||||
import toml
|
||||
import random
|
||||
from tqdm import tqdm
|
||||
|
||||
# 添加项目根目录到 Python 路径
|
||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
|
||||
sys.path.append(root_path)
|
||||
|
||||
# 加载配置文件
|
||||
config_path = os.path.join(root_path, "config", "bot_config.toml")
|
||||
with open(config_path, "r", encoding="utf-8") as f:
|
||||
config = toml.load(f)
|
||||
|
||||
# 现在可以导入src模块
|
||||
from individuality.not_using.scene import get_scene_by_factor, PERSONALITY_SCENES # noqa E402
|
||||
from individuality.not_using.questionnaire import FACTOR_DESCRIPTIONS # noqa E402
|
||||
from individuality.not_using.offline_llm import LLMRequestOff # noqa E402
|
||||
|
||||
# 加载环境变量
|
||||
env_path = os.path.join(root_path, ".env")
|
||||
if os.path.exists(env_path):
|
||||
print(f"从 {env_path} 加载环境变量")
|
||||
load_dotenv(env_path)
|
||||
else:
|
||||
print(f"未找到环境变量文件: {env_path}")
|
||||
print("将使用默认配置")
|
||||
|
||||
|
||||
def adapt_scene(scene: str) -> str:
|
||||
personality_core = config["personality"]["personality_core"]
|
||||
personality_side = config["personality"]["personality_side"]
|
||||
personality_side = random.choice(personality_side)
|
||||
identitys = config["identity"]["identity"]
|
||||
identity = random.choice(identitys)
|
||||
|
||||
"""
|
||||
根据config中的属性，改编场景使其更适合当前角色
|
||||
|
||||
Args:
|
||||
scene: 原始场景描述
|
||||
|
||||
Returns:
|
||||
str: 改编后的场景描述
|
||||
"""
|
||||
try:
|
||||
prompt = f"""
|
||||
这是一个参与人格测评的角色形象:
|
||||
- 昵称: {config["bot"]["nickname"]}
|
||||
- 性别: {config["identity"]["gender"]}
|
||||
- 年龄: {config["identity"]["age"]}岁
|
||||
- 外貌: {config["identity"]["appearance"]}
|
||||
- 性格核心: {personality_core}
|
||||
- 性格侧面: {personality_side}
|
||||
- 身份细节: {identity}
|
||||
|
||||
请根据上述形象，改编以下场景，在测评中，用户将根据该场景给出上述角色形象的反应:
|
||||
{scene}
|
||||
保持场景的本质不变，但最好贴近生活且具体，并且让它更适合这个角色。
|
||||
改编后的场景应该自然、连贯，并考虑角色的年龄、身份和性格特点。只返回改编后的场景描述，不要包含其他说明。注意{config["bot"]["nickname"]}是面对这个场景的人，而不是场景的其他人。场景中不会有其描述，
|
||||
现在，请你给出改编后的场景描述
|
||||
"""
|
||||
|
||||
llm = LLMRequestOff(model_name=config["model"]["llm_normal"]["name"])
|
||||
adapted_scene, _ = llm.generate_response(prompt)
|
||||
|
||||
# 检查返回的场景是否为空或错误信息
|
||||
if not adapted_scene or "错误" in adapted_scene or "失败" in adapted_scene:
|
||||
print("场景改编失败，将使用原始场景")
|
||||
return scene
|
||||
|
||||
return adapted_scene
|
||||
except Exception as e:
|
||||
print(f"场景改编过程出错：{str(e)}，将使用原始场景")
|
||||
return scene
|
||||
|
||||
|
||||
class PersonalityEvaluatorDirect:
|
||||
def __init__(self):
|
||||
self.personality_traits = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
|
||||
self.scenarios = []
|
||||
self.final_scores: Dict[str, float] = {"开放性": 0, "严谨性": 0, "外向性": 0, "宜人性": 0, "神经质": 0}
|
||||
self.dimension_counts = {trait: 0 for trait in self.final_scores}
|
||||
|
||||
# 为每个人格特质获取对应的场景
|
||||
for trait in PERSONALITY_SCENES:
|
||||
scenes = get_scene_by_factor(trait)
|
||||
if not scenes:
|
||||
continue
|
||||
|
||||
# 从每个维度选择3个场景
|
||||
import random
|
||||
|
||||
scene_keys = list(scenes.keys())
|
||||
selected_scenes = random.sample(scene_keys, min(3, len(scene_keys)))
|
||||
|
||||
for scene_key in selected_scenes:
|
||||
scene = scenes[scene_key]
|
||||
|
||||
# 为每个场景添加评估维度
|
||||
# 主维度是当前特质，次维度随机选择一个其他特质
|
||||
other_traits = [t for t in PERSONALITY_SCENES if t != trait]
|
||||
secondary_trait = random.choice(other_traits)
|
||||
|
||||
self.scenarios.append(
|
||||
{"场景": scene["scenario"], "评估维度": [trait, secondary_trait], "场景编号": scene_key}
|
||||
)
|
||||
|
||||
self.llm = LLMRequestOff()
|
||||
|
||||
def evaluate_response(self, scenario: str, response: str, dimensions: List[str]) -> Dict[str, float]:
|
||||
"""
|
||||
使用 DeepSeek AI 评估用户对特定场景的反应
|
||||
"""
|
||||
# 构建维度描述
|
||||
dimension_descriptions = []
|
||||
for dim in dimensions:
|
||||
if desc := FACTOR_DESCRIPTIONS.get(dim, ""):
|
||||
dimension_descriptions.append(f"- {dim}：{desc}")
|
||||
|
||||
dimensions_text = "\n".join(dimension_descriptions)
|
||||
|
||||
prompt = f"""请根据以下场景和用户描述，评估用户在大五人格模型中的相关维度得分（1-6分）。
|
||||
|
||||
场景描述：
|
||||
{scenario}
|
||||
|
||||
用户回应：
|
||||
{response}
|
||||
|
||||
需要评估的维度说明：
|
||||
{dimensions_text}
|
||||
|
||||
请按照以下格式输出评估结果（仅输出JSON格式）：
|
||||
{{
|
||||
"{dimensions[0]}": 分数,
|
||||
"{dimensions[1]}": 分数
|
||||
}}
|
||||
|
||||
评分标准：
|
||||
1 = 非常不符合该维度特征
|
||||
2 = 比较不符合该维度特征
|
||||
3 = 有点不符合该维度特征
|
||||
4 = 有点符合该维度特征
|
||||
5 = 比较符合该维度特征
|
||||
6 = 非常符合该维度特征
|
||||
|
||||
请根据用户的回应，结合场景和维度说明进行评分。确保分数在1-6之间，并给出合理的评估。"""
|
||||
|
||||
try:
|
||||
ai_response, _ = self.llm.generate_response(prompt)
|
||||
# 尝试从AI响应中提取JSON部分
|
||||
start_idx = ai_response.find("{")
|
||||
end_idx = ai_response.rfind("}") + 1
|
||||
if start_idx != -1 and end_idx != 0:
|
||||
json_str = ai_response[start_idx:end_idx]
|
||||
scores = json.loads(json_str)
|
||||
# 确保所有分数在1-6之间
|
||||
return {k: max(1, min(6, float(v))) for k, v in scores.items()}
|
||||
else:
|
||||
print("AI响应格式不正确，使用默认评分")
|
||||
return {dim: 3.5 for dim in dimensions}
|
||||
except Exception as e:
|
||||
print(f"评估过程出错：{str(e)}")
|
||||
return {dim: 3.5 for dim in dimensions}
|
||||
|
||||
def run_evaluation(self):
|
||||
"""
|
||||
运行整个评估过程
|
||||
"""
|
||||
print(f"欢迎使用{config['bot']['nickname']}形象创建程序！")
|
||||
print("接下来，将给您呈现一系列有关您bot的场景（共15个）。")
|
||||
print("请想象您的bot在以下场景下会做什么，并描述您的bot的反应。")
|
||||
print("每个场景都会进行不同方面的评估。")
|
||||
print("\n角色基本信息：")
|
||||
print(f"- 昵称：{config['bot']['nickname']}")
|
||||
print(f"- 性格核心：{config['personality']['personality_core']}")
|
||||
print(f"- 性格侧面：{config['personality']['personality_side']}")
|
||||
print(f"- 身份细节：{config['identity']['identity']}")
|
||||
print("\n准备好了吗？按回车键开始...")
|
||||
input()
|
||||
|
||||
total_scenarios = len(self.scenarios)
|
||||
progress_bar = tqdm(
|
||||
total=total_scenarios,
|
||||
desc="场景进度",
|
||||
ncols=100,
|
||||
bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]",
|
||||
)
|
||||
|
||||
for _i, scenario_data in enumerate(self.scenarios, 1):
|
||||
# print(f"\n{'-' * 20} 场景 {i}/{total_scenarios} - {scenario_data['场景编号']} {'-' * 20}")
|
||||
|
||||
# 改编场景，使其更适合当前角色
|
||||
print(f"{config['bot']['nickname']}祈祷中...")
|
||||
adapted_scene = adapt_scene(scenario_data["场景"])
|
||||
scenario_data["改编场景"] = adapted_scene
|
||||
|
||||
print(adapted_scene)
|
||||
print(f"\n请描述{config['bot']['nickname']}在这种情况下会如何反应：")
|
||||
response = input().strip()
|
||||
|
||||
if not response:
|
||||
print("反应描述不能为空！")
|
||||
continue
|
||||
|
||||
print("\n正在评估您的描述...")
|
||||
scores = self.evaluate_response(adapted_scene, response, scenario_data["评估维度"])
|
||||
|
||||
# 更新最终分数
|
||||
for dimension, score in scores.items():
|
||||
self.final_scores[dimension] += score
|
||||
self.dimension_counts[dimension] += 1
|
||||
|
||||
print("\n当前评估结果：")
|
||||
print("-" * 30)
|
||||
for dimension, score in scores.items():
|
||||
print(f"{dimension}: {score}/6")
|
||||
|
||||
# 更新进度条
|
||||
progress_bar.update(1)
|
||||
|
||||
# if i < total_scenarios:
|
||||
# print("\n按回车键继续下一个场景...")
|
||||
# input()
|
||||
|
||||
progress_bar.close()
|
||||
|
||||
# 计算平均分
|
||||
for dimension in self.final_scores:
|
||||
if self.dimension_counts[dimension] > 0:
|
||||
self.final_scores[dimension] = round(self.final_scores[dimension] / self.dimension_counts[dimension], 2)
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print(f" {config['bot']['nickname']}的人格特征评估结果 ".center(50))
|
||||
print("=" * 50)
|
||||
for trait, score in self.final_scores.items():
|
||||
print(f"{trait}: {score}/6".ljust(20) + f"测试场景数：{self.dimension_counts[trait]}".rjust(30))
|
||||
print("=" * 50)
|
||||
|
||||
# 返回评估结果
|
||||
return self.get_result()
|
||||
|
||||
def get_result(self):
|
||||
"""
|
||||
获取评估结果
|
||||
"""
|
||||
return {
|
||||
"final_scores": self.final_scores,
|
||||
"dimension_counts": self.dimension_counts,
|
||||
"scenarios": self.scenarios,
|
||||
"bot_info": {
|
||||
"nickname": config["bot"]["nickname"],
|
||||
"gender": config["identity"]["gender"],
|
||||
"age": config["identity"]["age"],
|
||||
"height": config["identity"]["height"],
|
||||
"weight": config["identity"]["weight"],
|
||||
"appearance": config["identity"]["appearance"],
|
||||
"personality_core": config["personality"]["personality_core"],
|
||||
"personality_side": config["personality"]["personality_side"],
|
||||
"identity": config["identity"]["identity"],
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
evaluator = PersonalityEvaluatorDirect()
|
||||
result = evaluator.run_evaluation()
|
||||
|
||||
# 准备简化的结果数据
|
||||
simplified_result = {
|
||||
"openness": round(result["final_scores"]["开放性"] / 6, 1), # 转换为0-1范围
|
||||
"conscientiousness": round(result["final_scores"]["严谨性"] / 6, 1),
|
||||
"extraversion": round(result["final_scores"]["外向性"] / 6, 1),
|
||||
"agreeableness": round(result["final_scores"]["宜人性"] / 6, 1),
|
||||
"neuroticism": round(result["final_scores"]["神经质"] / 6, 1),
|
||||
"bot_nickname": config["bot"]["nickname"],
|
||||
}
|
||||
|
||||
# 确保目录存在
|
||||
save_dir = os.path.join(root_path, "data", "personality")
|
||||
os.makedirs(save_dir, exist_ok=True)
|
||||
|
||||
# 创建文件名，替换可能的非法字符
|
||||
bot_name = config["bot"]["nickname"]
|
||||
# 替换Windows文件名中不允许的字符
|
||||
for char in ["\\", "/", ":", "*", "?", '"', "<", ">", "|"]:
|
||||
bot_name = bot_name.replace(char, "_")
|
||||
|
||||
file_name = f"{bot_name}_personality.per"
|
||||
save_path = os.path.join(save_dir, file_name)
|
||||
|
||||
# 保存简化的结果
|
||||
with open(save_path, "w", encoding="utf-8") as f:
|
||||
json.dump(simplified_result, f, ensure_ascii=False, indent=4)
|
||||
|
||||
print(f"\n结果已保存到 {save_path}")
|
||||
|
||||
# 同时保存完整结果到results目录
|
||||
os.makedirs("results", exist_ok=True)
|
||||
with open("results/personality_result.json", "w", encoding="utf-8") as f:
|
||||
json.dump(result, f, ensure_ascii=False, indent=2)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -1,142 +0,0 @@
|
|||
# 人格测试问卷题目
|
||||
# 王孟成, 戴晓阳, & 姚树桥. (2011).
|
||||
# 中国大五人格问卷的初步编制Ⅲ:简式版的制定及信效度检验. 中国临床心理学杂志, 19(04), Article 04.
|
||||
|
||||
# 王孟成, 戴晓阳, & 姚树桥. (2010).
|
||||
# 中国大五人格问卷的初步编制Ⅰ:理论框架与信度分析. 中国临床心理学杂志, 18(05), Article 05.
|
||||
|
||||
PERSONALITY_QUESTIONS = [
|
||||
# 神经质维度 (F1)
|
||||
{"id": 1, "content": "我常担心有什么不好的事情要发生", "factor": "神经质", "reverse_scoring": False},
|
||||
{"id": 2, "content": "我常感到害怕", "factor": "神经质", "reverse_scoring": False},
|
||||
{"id": 3, "content": "有时我觉得自己一无是处", "factor": "神经质", "reverse_scoring": False},
|
||||
{"id": 4, "content": "我很少感到忧郁或沮丧", "factor": "神经质", "reverse_scoring": True},
|
||||
{"id": 5, "content": "别人一句漫不经心的话，我常会联系在自己身上", "factor": "神经质", "reverse_scoring": False},
|
||||
{"id": 6, "content": "在面对压力时，我有种快要崩溃的感觉", "factor": "神经质", "reverse_scoring": False},
|
||||
{"id": 7, "content": "我常担忧一些无关紧要的事情", "factor": "神经质", "reverse_scoring": False},
|
||||
{"id": 8, "content": "我常常感到内心不踏实", "factor": "神经质", "reverse_scoring": False},
|
||||
# 严谨性维度 (F2)
|
||||
{"id": 9, "content": "在工作上，我常只求能应付过去便可", "factor": "严谨性", "reverse_scoring": True},
|
||||
{"id": 10, "content": "一旦确定了目标，我会坚持努力地实现它", "factor": "严谨性", "reverse_scoring": False},
|
||||
{"id": 11, "content": "我常常是仔细考虑之后才做出决定", "factor": "严谨性", "reverse_scoring": False},
|
||||
{"id": 12, "content": "别人认为我是个慎重的人", "factor": "严谨性", "reverse_scoring": False},
|
||||
{"id": 13, "content": "做事讲究逻辑和条理是我的一个特点", "factor": "严谨性", "reverse_scoring": False},
|
||||
{"id": 14, "content": "我喜欢一开头就把事情计划好", "factor": "严谨性", "reverse_scoring": False},
|
||||
{"id": 15, "content": "我工作或学习很勤奋", "factor": "严谨性", "reverse_scoring": False},
|
||||
{"id": 16, "content": "我是个倾尽全力做事的人", "factor": "严谨性", "reverse_scoring": False},
|
||||
# 宜人性维度 (F3)
|
||||
{
|
||||
"id": 17,
|
||||
"content": "尽管人类社会存在着一些阴暗的东西（如战争、罪恶、欺诈），我仍然相信人性总的来说是善良的",
|
||||
"factor": "宜人性",
|
||||
"reverse_scoring": False,
|
||||
},
|
||||
{"id": 18, "content": "我觉得大部分人基本上是心怀善意的", "factor": "宜人性", "reverse_scoring": False},
|
||||
{"id": 19, "content": "虽然社会上有骗子，但我觉得大部分人还是可信的", "factor": "宜人性", "reverse_scoring": False},
|
||||
{"id": 20, "content": "我不太关心别人是否受到不公正的待遇", "factor": "宜人性", "reverse_scoring": True},
|
||||
{"id": 21, "content": "我时常觉得别人的痛苦与我无关", "factor": "宜人性", "reverse_scoring": True},
|
||||
{"id": 22, "content": "我常为那些遭遇不幸的人感到难过", "factor": "宜人性", "reverse_scoring": False},
|
||||
{"id": 23, "content": "我是那种只照顾好自己，不替别人担忧的人", "factor": "宜人性", "reverse_scoring": True},
|
||||
{"id": 24, "content": "当别人向我诉说不幸时，我常感到难过", "factor": "宜人性", "reverse_scoring": False},
|
||||
# 开放性维度 (F4)
|
||||
{"id": 25, "content": "我的想象力相当丰富", "factor": "开放性", "reverse_scoring": False},
|
||||
{"id": 26, "content": "我头脑中经常充满生动的画面", "factor": "开放性", "reverse_scoring": False},
|
||||
{"id": 27, "content": "我对许多事情有着很强的好奇心", "factor": "开放性", "reverse_scoring": False},
|
||||
{"id": 28, "content": "我喜欢冒险", "factor": "开放性", "reverse_scoring": False},
|
||||
{"id": 29, "content": "我是个勇于冒险，突破常规的人", "factor": "开放性", "reverse_scoring": False},
|
||||
{"id": 30, "content": "我身上具有别人没有的冒险精神", "factor": "开放性", "reverse_scoring": False},
|
||||
{
|
||||
"id": 31,
|
||||
"content": "我渴望学习一些新东西，即使它们与我的日常生活无关",
|
||||
"factor": "开放性",
|
||||
"reverse_scoring": False,
|
||||
},
|
||||
{
|
||||
"id": 32,
|
||||
"content": "我很愿意也很容易接受那些新事物、新观点、新想法",
|
||||
"factor": "开放性",
|
||||
"reverse_scoring": False,
|
||||
},
|
||||
# 外向性维度 (F5)
|
||||
{"id": 33, "content": "我喜欢参加社交与娱乐聚会", "factor": "外向性", "reverse_scoring": False},
|
||||
{"id": 34, "content": "我对人多的聚会感到乏味", "factor": "外向性", "reverse_scoring": True},
|
||||
{"id": 35, "content": "我尽量避免参加人多的聚会和嘈杂的环境", "factor": "外向性", "reverse_scoring": True},
|
||||
{"id": 36, "content": "在热闹的聚会上，我常常表现主动并尽情玩耍", "factor": "外向性", "reverse_scoring": False},
|
||||
{"id": 37, "content": "有我在的场合一般不会冷场", "factor": "外向性", "reverse_scoring": False},
|
||||
{"id": 38, "content": "我希望成为领导者而不是被领导者", "factor": "外向性", "reverse_scoring": False},
|
||||
{"id": 39, "content": "在一个团体中，我希望处于领导地位", "factor": "外向性", "reverse_scoring": False},
|
||||
{"id": 40, "content": "别人多认为我是一个热情和友好的人", "factor": "外向性", "reverse_scoring": False},
|
||||
]
|
||||
|
||||
# 因子维度说明
|
||||
FACTOR_DESCRIPTIONS = {
|
||||
"外向性": {
|
||||
"description": "反映个体神经系统的强弱和动力特征。外向性主要表现为个体在人际交往和社交活动中的倾向性，"
|
||||
"包括对社交活动的兴趣、"
|
||||
"对人群的态度、社交互动中的主动程度以及在群体中的影响力。高分者倾向于积极参与社交活动，乐于与人交往，善于表达自我，"
|
||||
"并往往在群体中发挥领导作用；低分者则倾向于独处，不喜欢热闹的社交场合，表现出内向、安静的特征。",
|
||||
"trait_words": ["热情", "活力", "社交", "主动"],
|
||||
"subfactors": {
|
||||
"合群性": "个体愿意与他人聚在一起，即接近人群的倾向；高分表现乐群、好交际，低分表现封闭、独处",
|
||||
"热情": "个体对待别人时所表现出的态度；高分表现热情好客，低分表现冷淡",
|
||||
"支配性": "个体喜欢指使、操纵他人，倾向于领导别人的特点；高分表现好强、发号施令，低分表现顺从、低调",
|
||||
"活跃": "个体精力充沛，活跃、主动性等特点；高分表现活跃，低分表现安静",
|
||||
},
|
||||
},
|
||||
"神经质": {
|
||||
"description": "反映个体情绪的状态和体验内心苦恼的倾向性。这个维度主要关注个体在面对压力、"
|
||||
"挫折和日常生活挑战时的情绪稳定性和适应能力。它包含了对焦虑、抑郁、愤怒等负面情绪的敏感程度，"
|
||||
"以及个体对这些情绪的调节和控制能力。高分者容易体验负面情绪，对压力较为敏感，情绪波动较大；"
|
||||
"低分者则表现出较强的情绪稳定性，能够较好地应对压力和挫折。",
|
||||
"trait_words": ["稳定", "沉着", "从容", "坚韧"],
|
||||
"subfactors": {
|
||||
"焦虑": "个体体验焦虑感的个体差异；高分表现坐立不安，低分表现平静",
|
||||
"抑郁": "个体体验抑郁情感的个体差异；高分表现郁郁寡欢，低分表现平静",
|
||||
"敏感多疑": "个体常常关注自己的内心活动，行为和过于意识人对自己的看法、评价；高分表现敏感多疑，"
|
||||
"低分表现淡定、自信",
|
||||
"脆弱性": "个体在危机或困难面前无力、脆弱的特点；高分表现无能、易受伤、逃避，低分表现坚强",
|
||||
"愤怒-敌意": "个体准备体验愤怒，及相关情绪的状态；高分表现暴躁易怒，低分表现平静",
|
||||
},
|
||||
},
|
||||
"严谨性": {
|
||||
"description": "反映个体在目标导向行为上的组织、坚持和动机特征。这个维度体现了个体在工作、"
|
||||
"学习等目标性活动中的自我约束和行为管理能力。它涉及到个体的责任感、自律性、计划性、条理性以及完成任务的态度。"
|
||||
"高分者往往表现出强烈的责任心、良好的组织能力、谨慎的决策风格和持续的努力精神；低分者则可能表现出随意性强、"
|
||||
"缺乏规划、做事马虎或易放弃的特点。",
|
||||
"trait_words": ["负责", "自律", "条理", "勤奋"],
|
||||
"subfactors": {
|
||||
"责任心": "个体对待任务和他人认真负责，以及对自己承诺的信守；高分表现有责任心、负责任，"
|
||||
"低分表现推卸责任、逃避处罚",
|
||||
"自我控制": "个体约束自己的能力，及自始至终的坚持性；高分表现自制、有毅力，低分表现冲动、无毅力",
|
||||
"审慎性": "个体在采取具体行动前的心理状态；高分表现谨慎、小心，低分表现鲁莽、草率",
|
||||
"条理性": "个体处理事务和工作的秩序，条理和逻辑性；高分表现整洁、有秩序，低分表现混乱、遗漏",
|
||||
"勤奋": "个体工作和学习的努力程度及为达到目标而表现出的进取精神；高分表现勤奋、刻苦，低分表现懒散",
|
||||
},
|
||||
},
|
||||
"开放性": {
|
||||
"description": "反映个体对新异事物、新观念和新经验的接受程度，以及在思维和行为方面的创新倾向。"
|
||||
"这个维度体现了个体在认知和体验方面的广度、深度和灵活性。它包括对艺术的欣赏能力、对知识的求知欲、想象力的丰富程度，"
|
||||
"以及对冒险和创新的态度。高分者往往具有丰富的想象力、广泛的兴趣、开放的思维方式和创新的倾向；低分者则倾向于保守、"
|
||||
"传统，喜欢熟悉和常规的事物。",
|
||||
"trait_words": ["创新", "好奇", "艺术", "冒险"],
|
||||
"subfactors": {
|
||||
"幻想": "个体富于幻想和想象的水平；高分表现想象力丰富，低分表现想象力匮乏",
|
||||
"审美": "个体对于艺术和美的敏感与热爱程度；高分表现富有艺术气息，低分表现一般对艺术不敏感",
|
||||
"好奇心": "个体对未知事物的态度；高分表现兴趣广泛、好奇心浓，低分表现兴趣少、无好奇心",
|
||||
"冒险精神": "个体愿意尝试有风险活动的个体差异；高分表现好冒险，低分表现保守",
|
||||
"价值观念": "个体对新事物、新观念、怪异想法的态度；高分表现开放、坦然接受新事物，低分则相反",
|
||||
},
|
||||
},
|
||||
"宜人性": {
|
||||
"description": "反映个体在人际关系中的亲和倾向，体现了对他人的关心、同情和合作意愿。"
|
||||
"这个维度主要关注个体与他人互动时的态度和行为特征，包括对他人的信任程度、同理心水平、"
|
||||
"助人意愿以及在人际冲突中的处理方式。高分者通常表现出友善、富有同情心、乐于助人的特质，善于与他人建立和谐关系；"
|
||||
"低分者则可能表现出较少的人际关注，在社交互动中更注重自身利益，较少考虑他人感受。",
|
||||
"trait_words": ["友善", "同理", "信任", "合作"],
|
||||
"subfactors": {
|
||||
"信任": "个体对他人和/或他人言论的相信程度；高分表现信任他人，低分表现怀疑",
|
||||
"体贴": "个体对别人的兴趣和需要的关注程度；高分表现体贴、温存，低分表现冷漠、不在乎",
|
||||
"同情": "个体对处于不利地位的人或物的态度；高分表现富有同情心，低分表现冷漠",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
|
@ -1,43 +0,0 @@
|
|||
import json
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
|
||||
def load_scenes() -> dict[str, Any]:
|
||||
"""
|
||||
从JSON文件加载场景数据
|
||||
|
||||
Returns:
|
||||
Dict: 包含所有场景的字典
|
||||
"""
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
json_path = os.path.join(current_dir, "template_scene.json")
|
||||
|
||||
with open(json_path, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
PERSONALITY_SCENES = load_scenes()
|
||||
|
||||
|
||||
def get_scene_by_factor(factor: str) -> dict | None:
|
||||
"""
|
||||
根据人格因子获取对应的情景测试
|
||||
|
||||
Args:
|
||||
factor (str): 人格因子名称
|
||||
|
||||
Returns:
|
||||
dict: 包含情景描述的字典
|
||||
"""
|
||||
return PERSONALITY_SCENES.get(factor, None)
|
||||
|
||||
|
||||
def get_all_scenes() -> dict:
|
||||
"""
|
||||
获取所有情景测试
|
||||
|
||||
Returns:
|
||||
Dict: 所有情景测试的字典
|
||||
"""
|
||||
return PERSONALITY_SCENES
|
||||
|
|
@ -1,112 +0,0 @@
|
|||
{
|
||||
"外向性": {
|
||||
"场景1": {
|
||||
"scenario": "你刚刚搬到一个新的城市工作。今天是你入职的第一天，在公司的电梯里，一位同事微笑着和你打招呼：\n\n同事：「嗨！你是新来的同事吧？我是市场部的小林。」\n\n同事看起来很友善，还主动介绍说：「待会午饭时间，我们部门有几个人准备一起去楼下新开的餐厅，你要一起来吗？可以认识一下其他同事。」",
|
||||
"explanation": "这个场景通过职场社交情境，观察个体对于新环境、新社交圈的态度和反应倾向。"
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": "在大学班级群里，班长发起了一个组织班级联谊活动的投票：\n\n班长：「大家好！下周末我们准备举办一次班级联谊活动，地点在学校附近的KTV。想请大家报名参加，也欢迎大家邀请其他班级的同学！」\n\n已经有几个同学在群里积极响应，有人@你问你要不要一起参加。",
|
||||
"explanation": "通过班级活动场景，观察个体对群体社交活动的参与意愿。"
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": "你在社交平台上发布了一条动态，收到了很多陌生网友的评论和私信：\n\n网友A：「你说的这个观点很有意思！想和你多交流一下。」\n\n网友B：「我也对这个话题很感兴趣，要不要建个群一起讨论？」",
|
||||
"explanation": "通过网络社交场景，观察个体对线上社交的态度。"
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": "你暗恋的对象今天主动来找你：\n\n对方：「那个...我最近在准备一个演讲比赛，听说你口才很好。能不能请你帮我看看演讲稿，顺便给我一些建议？如果你有时间的话，可以一起吃个饭聊聊。」",
|
||||
"explanation": "通过恋爱情境，观察个体在面对心仪对象时的社交表现。"
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": "在一次线下读书会上，主持人突然点名让你分享读后感：\n\n主持人：「听说你对这本书很有见解，能不能和大家分享一下你的想法？」\n\n现场有二十多个陌生的读书爱好者，都期待地看着你。",
|
||||
"explanation": "通过即兴发言场景，观察个体的社交表现欲和公众表达能力。"
|
||||
}
|
||||
},
|
||||
"神经质": {
|
||||
"场景1": {
|
||||
"scenario": "你正在准备一个重要的项目演示，这关系到你的晋升机会。就在演示前30分钟，你收到了主管发来的消息：\n\n主管：「临时有个变动，CEO也会来听你的演示。他对这个项目特别感兴趣。」\n\n正当你准备回复时，主管又发来一条：「对了，能不能把演示时间压缩到15分钟？CEO下午还有其他安排。你之前准备的是30分钟的版本对吧？」",
|
||||
"explanation": "这个场景通过突发的压力情境，观察个体在面对计划外变化时的情绪反应和调节能力。"
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": "期末考试前一天晚上，你收到了好朋友发来的消息：\n\n好朋友：「不好意思这么晚打扰你...我看你平时成绩很好，能不能帮我解答几个问题？我真的很担心明天的考试。」\n\n你看了看时间，已经是晚上11点，而你原本计划的复习还没完成。",
|
||||
"explanation": "通过考试压力场景，观察个体在时间紧张时的情绪管理。"
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": "你在社交媒体上发表的一个观点引发了争议，有不少人开始批评你：\n\n网友A：「这种观点也好意思说出来，真是无知。」\n\n网友B：「建议楼主先去补补课再来发言。」\n\n评论区里的负面评论越来越多，还有人开始人身攻击。",
|
||||
"explanation": "通过网络争议场景，观察个体面对批评时的心理承受能力。"
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": "你和恋人约好今天一起看电影，但在约定时间前半小时，对方发来消息：\n\n恋人：「对不起，我临时有点事，可能要迟到一会儿。」\n\n二十分钟后，对方又发来消息：「可能要再等等，抱歉！」\n\n电影快要开始了，但对方还是没有出现。",
|
||||
"explanation": "通过恋爱情境，观察个体对不确定性的忍耐程度。"
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": "在一次重要的小组展示中，你的组员在演示途中突然卡壳了：\n\n组员小声对你说：「我忘词了，接下来的部分是什么来着...」\n\n台下的老师和同学都在等待，气氛有些尴尬。",
|
||||
"explanation": "通过公开场合的突发状况，观察个体的应急反应和压力处理能力。"
|
||||
}
|
||||
},
|
||||
"严谨性": {
|
||||
"场景1": {
|
||||
"scenario": "你是团队的项目负责人，刚刚接手了一个为期两个月的重要项目。在第一次团队会议上：\n\n小王：「老大，我觉得两个月时间很充裕，我们先做着看吧，遇到问题再解决。」\n\n小张：「要不要先列个时间表？不过感觉太详细的计划也没必要，点到为止就行。」\n\n小李：「客户那边说如果能提前完成有奖励，我觉得我们可以先做快一点的部分。」",
|
||||
"explanation": "这个场景通过项目管理情境，体现个体在工作方法、计划性和责任心方面的特征。"
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": "期末小组作业，组长让大家分工完成一份研究报告。在截止日期前三天：\n\n组员A：「我的部分大概写完了，感觉还行。」\n\n组员B：「我这边可能还要一天才能完成，最近太忙了。」\n\n组员C发来一份没有任何引用出处、可能存在抄袭的内容：「我写完了，你们看看怎么样？」",
|
||||
"explanation": "通过学习场景，观察个体对学术规范和质量要求的重视程度。"
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": "你在一个兴趣小组的群聊中，大家正在讨论举办一次线下活动：\n\n成员A：「到时候见面就知道具体怎么玩了！」\n\n成员B：「对啊，随意一点挺好的。」\n\n成员C：「人来了自然就热闹了。」",
|
||||
"explanation": "通过活动组织场景，观察个体对活动计划的态度。"
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": "你的好友小明邀请你一起参加一个重要的演出活动，他说：\n\n小明：「到时候我们就即兴发挥吧！不用排练了，我相信我们的默契。」\n\n距离演出还有三天，但节目内容、配乐和服装都还没有确定。",
|
||||
"explanation": "通过演出准备场景，观察个体的计划性和对不确定性的接受程度。"
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": "在一个重要的团队项目中，你发现一个同事的工作存在明显错误：\n\n同事：「差不多就行了，反正领导也看不出来。」\n\n这个错误可能不会立即造成问题，但长期来看可能会影响项目质量。",
|
||||
"explanation": "通过工作质量场景，观察个体对细节和标准的坚持程度。"
|
||||
}
|
||||
},
|
||||
"开放性": {
|
||||
"场景1": {
|
||||
"scenario": "周末下午，你的好友小美兴致勃勃地给你打电话：\n\n小美：「我刚发现一个特别有意思的沉浸式艺术展！不是传统那种挂画的展览，而是把整个空间都变成了艺术品。观众要穿特制的服装，还要带上VR眼镜，好像还有AI实时互动！」\n\n小美继续说：「虽然票价不便宜，但听说体验很独特。网上评价两极分化，有人说是前所未有的艺术革新，也有人说是哗众取宠。要不要周末一起去体验一下？」",
|
||||
"explanation": "这个场景通过新型艺术体验，反映个体对创新事物的接受程度和尝试意愿。"
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": "在一节创意写作课上，老师提出了一个特别的作业：\n\n老师：「下周的作业是用AI写作工具协助创作一篇小说。你们可以自由探索如何与AI合作，打破传统写作方式。」\n\n班上随即展开了激烈讨论，有人认为这是对创作的亵渎，也有人对这种新形式感到兴奋。",
|
||||
"explanation": "通过新技术应用场景，观察个体对创新学习方式的态度。"
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": "在社交媒体上，你看到一个朋友分享了一种新的学习方式：\n\n「最近我在尝试'沉浸式学习'，就是完全投入到一个全新的领域。比如学习一门陌生的语言，或者尝试完全不同的职业技能。虽然过程会很辛苦，但这种打破舒适圈的感觉真的很棒！」\n\n评论区里争论不断，有人认为这种学习方式效率高，也有人觉得太激进。",
|
||||
"explanation": "通过新型学习方式，观察个体对创新和挑战的态度。"
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": "你的朋友向你推荐了一种新的饮食方式：\n\n朋友：「我最近在尝试'未来食品'，比如人造肉、3D打印食物、昆虫蛋白等。这不仅对环境友好，营养也很均衡。要不要一起来尝试看看？」\n\n这个提议让你感到好奇又犹豫，你之前从未尝试过这些新型食物。",
|
||||
"explanation": "通过饮食创新场景，观察个体对新事物的接受度和尝试精神。"
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": "在一次朋友聚会上，大家正在讨论未来职业规划：\n\n朋友A：「我准备辞职去做自媒体，专门介绍一些小众的文化和艺术。」\n\n朋友B：「我想去学习生物科技，准备转行做人造肉研发。」\n\n朋友C：「我在考虑加入一个区块链创业项目，虽然风险很大。」",
|
||||
"explanation": "通过职业选择场景，观察个体对新兴领域的探索意愿。"
|
||||
}
|
||||
},
|
||||
"宜人性": {
|
||||
"场景1": {
|
||||
"scenario": "在回家的公交车上，你遇到这样一幕：\n\n一位老奶奶颤颤巍巍地上了车，车上座位已经坐满了。她站在你旁边，看起来很疲惫。这时你听到前排两个年轻人的对话：\n\n年轻人A：「那个老太太好像站不稳，看起来挺累的。」\n\n年轻人B：「现在的老年人真是...我看她包里还有菜，肯定是去菜市场买完菜回来的，这么多人都不知道叫子女开车接送。」\n\n就在这时，老奶奶一个趔趄，差点摔倒。她扶住了扶手，但包里的东西洒了一些出来。",
|
||||
"explanation": "这个场景通过公共场合的助人情境，体现个体的同理心和对他人需求的关注程度。"
|
||||
},
|
||||
"场景2": {
|
||||
"scenario": "在班级群里，有同学发起为生病住院的同学捐款：\n\n同学A：「大家好，小林最近得了重病住院，医药费很贵，家里负担很重。我们要不要一起帮帮他？」\n\n同学B：「我觉得这是他家里的事，我们不方便参与吧。」\n\n同学C：「但是都是同学一场，帮帮忙也是应该的。」",
|
||||
"explanation": "通过同学互助场景，观察个体的助人意愿和同理心。"
|
||||
},
|
||||
"场景3": {
|
||||
"scenario": "在一个网络讨论组里，有人发布了求助信息：\n\n求助者：「最近心情很低落，感觉生活很压抑，不知道该怎么办...」\n\n评论区里已经有一些回复：\n「生活本来就是这样，想开点！」\n「你这样子太消极了，要积极面对。」\n「谁还没点烦心事啊，过段时间就好了。」",
|
||||
"explanation": "通过网络互助场景，观察个体的共情能力和安慰方式。"
|
||||
},
|
||||
"场景4": {
|
||||
"scenario": "你的朋友向你倾诉工作压力：\n\n朋友：「最近工作真的好累，感觉快坚持不下去了...」\n\n但今天你也遇到了很多烦心事，心情也不太好。",
|
||||
"explanation": "通过感情关系场景，观察个体在自身状态不佳时的关怀能力。"
|
||||
},
|
||||
"场景5": {
|
||||
"scenario": "在一次团队项目中，新来的同事小王因为经验不足，造成了一个严重的错误。在部门会议上：\n\n主管：「这个错误造成了很大的损失，是谁负责的这部分？」\n\n小王看起来很紧张，欲言又止。你知道是他造成的错误，同时你也是这个项目的共同负责人。",
|
||||
"explanation": "通过职场情境，观察个体在面对他人过错时的态度和处理方式。"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -10,7 +10,6 @@ from src.chat.message_receive.chat_stream import get_chat_manager
|
|||
from src.config.config import global_config
|
||||
from src.chat.message_receive.bot import chat_bot
|
||||
from src.common.logger import get_logger
|
||||
from src.individuality.individuality import get_individuality, Individuality
|
||||
from src.common.server import get_global_server, Server
|
||||
from src.mood.mood_manager import mood_manager
|
||||
from src.chat.knowledge import lpmm_start_up
|
||||
|
|
@ -43,8 +42,6 @@ class MainSystem:
|
|||
else:
|
||||
self.hippocampus_manager = None
|
||||
|
||||
self.individuality: Individuality = get_individuality()
|
||||
|
||||
# 使用消息API替代直接的FastAPI实例
|
||||
self.app: MessageServer = get_global_api()
|
||||
self.server: Server = get_global_server()
|
||||
|
|
@ -117,9 +114,6 @@ class MainSystem:
|
|||
|
||||
# 将bot.py中的chat_bot.message_process消息处理函数注册到api.py的消息处理基类中
|
||||
self.app.register_message_handler(chat_bot.message_process)
|
||||
|
||||
# 初始化个体特征
|
||||
await self.individuality.initialize()
|
||||
|
||||
await check_and_run_migrations()
|
||||
|
||||
|
|
|
|||
|
|
@ -637,7 +637,6 @@ class PersonInfoManager:
|
|||
current_name_set = set(self.person_name_list.values())
|
||||
|
||||
while current_try < max_retries:
|
||||
# prompt_personality =get_individuality().get_prompt(x_person=2, level=1)
|
||||
bot_name = global_config.bot.nickname
|
||||
|
||||
qv_name_prompt = f"你是{bot_name}，一个伪装成人类的AI，你不能让别人发现这一点，"
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
[inner]
|
||||
version = "6.5.0"
|
||||
version = "6.6.0"
|
||||
|
||||
#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
|
||||
#如果你想要修改配置文件，请递增version的值
|
||||
|
|
@ -22,6 +22,7 @@ alias_names = ["麦叠", "牢麦"] # 麦麦的别名
|
|||
personality_core = "是一个女孩子"
|
||||
# 人格的细节，描述人格的一些侧面
|
||||
personality_side = "有时候说话不过脑子,喜欢开玩笑, 有时候会表现得无语,有时候会喜欢说一些奇怪的话"
|
||||
|
||||
#アイデンティティがない 生まれないらららら
|
||||
# 可以描述外貌，性别，身高，职业，属性等等描述
|
||||
identity = "年龄为19岁,是女孩子,身高为160cm,有黑色的短发"
|
||||
|
|
@ -29,10 +30,11 @@ identity = "年龄为19岁,是女孩子,身高为160cm,有黑色的短发"
|
|||
# 描述麦麦说话的表达风格，表达习惯，如要修改，可以酌情新增内容
|
||||
reply_style = "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。不要浮夸，不要夸张修辞。"
|
||||
|
||||
# 描述麦麦的行为风格，会影响麦麦什么时候回复，什么时候使用动作，麦麦考虑的可就多了
|
||||
plan_style = "当你刚刚发送了消息，没有人回复时，不要选择action，如果有别的动作（非回复）满足条件，可以选择，当你一次发送了太多消息，为了避免打扰聊天节奏，不要选择动作"
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compress_personality = false # 是否压缩人格，压缩后会精简人格信息，节省token消耗并提高回复性能，但是会丢失一些信息，如果人设不长，可以关闭
|
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compress_identity = true # 是否压缩身份，压缩后会精简身份信息，节省token消耗并提高回复性能，但是会丢失一些信息，如果不长，可以关闭
|
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# 麦麦的兴趣，会影响麦麦对什么话题进行回复
|
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interest = "对技术相关话题，游戏和动漫相关话题感兴趣，也对日常话题感兴趣，不喜欢太过沉重严肃的话题"
|
||||
|
||||
[expression]
|
||||
# 表达学习配置
|
||||
|
|
|
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Loading…
Reference in New Issue