mirror of https://github.com/Mai-with-u/MaiBot.git
Update self_processor.py
2829798842
GitHub
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53cb094c79
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@ -7,63 +7,47 @@ import traceback
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from src.common.logger_manager import get_logger
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from src.individuality.individuality import individuality
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.chat.utils.json_utils import safe_json_dumps
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from src.chat.message_receive.chat_stream import chat_manager
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from src.person_info.relationship_manager import relationship_manager
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from .base_processor import BaseProcessor
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from src.chat.focus_chat.info.mind_info import MindInfo
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from typing import List, Optional
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from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
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from typing import Dict
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from src.chat.focus_chat.info.info_base import InfoBase
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from src.chat.focus_chat.info.self_info import SelfInfo
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logger = get_logger("processor")
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def init_prompt():
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group_prompt = """
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你的名字是{bot_name}
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{memory_str}
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{extra_info}
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indentify_prompt = """
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{name_block}
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你是一个AI，但是你伪装成了一个人类，你的人格是，{prompt_personality}
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{indentify_block}
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{relation_prompt}
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{cycle_info_block}
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{memory_str}
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现在是{time_now}，你正在上网，和qq群里的网友们聊天，以下是正在进行的聊天内容：
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{chat_observe_info}
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以下是你之前对聊天的观察和规划，你的名字是{bot_name}：
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{last_mind}
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现在请你根据现有的信息，思考自我认同：请严格遵守以下规则
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1. 请严格参考最上方的人设，适当参考记忆和当前聊天内容，不要被记忆和当前聊天内容中相反的内容误导
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2. 你是一个什么样的人,你和群里的人关系如何
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3. 你的形象是什么
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4. 思考有没有人提到你，或者图片与你有关
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5. 你的自我认同是否有助于你的回答，如果你需要自我相关的信息来帮你参与聊天，请输出，否则请输出十几个字的简短自我认同
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6. 一般情况下不用输出自我认同，只需要输出十几个字的简短自我认同就好，除非有明显需要自我认同的场景
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现在请你继续输出观察和规划，输出要求：
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1. 先关注未读新消息的内容和近期回复历史
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2. 根据新信息，修改和删除之前的观察和规划
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3. 根据聊天内容继续输出观察和规划
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4. 注意群聊的时间线索，话题由谁发起，进展状况如何，思考聊天的时间线。
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6. 语言简洁自然，不要分点，不要浮夸，不要修辞，仅输出思考内容就好"""
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Prompt(group_prompt, "sub_heartflow_prompt_before")
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输出内容平淡一些，说中文，不要浮夸，平淡一些。
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请注意不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出自我认同内容，记得明确说明这是你的自我认同。
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private_prompt = """
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你的名字是{bot_name}
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{memory_str}
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{extra_info}
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{relation_prompt}
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{cycle_info_block}
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现在是{time_now}，你正在上网，和qq群里的网友们聊天，以下是正在进行的聊天内容：
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{chat_observe_info}
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以下是你之前对聊天的观察和规划，你的名字是{bot_name}：
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{last_mind}
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现在请你继续输出观察和规划，输出要求：
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1. 先关注未读新消息的内容和近期回复历史
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2. 根据新信息，修改和删除之前的观察和规划
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3. 根据聊天内容继续输出观察和规划
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4. 注意群聊的时间线索，话题由谁发起，进展状况如何，思考聊天的时间线。
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6. 语言简洁自然，不要分点，不要浮夸，不要修辞，仅输出思考内容就好"""
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Prompt(private_prompt, "sub_heartflow_prompt_private_before")
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"""
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Prompt(indentify_prompt, "indentify_prompt")
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class MindProcessor(BaseProcessor):
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log_prefix = "聊天思考"
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class SelfProcessor(BaseProcessor):
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log_prefix = "自我认同"
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def __init__(self, subheartflow_id: str):
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super().__init__()
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@ -71,48 +55,14 @@ class MindProcessor(BaseProcessor):
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self.subheartflow_id = subheartflow_id
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self.llm_model = LLMRequest(
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model=global_config.model.focus_chat_mind,
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temperature=global_config.model.focus_chat_mind["temp"],
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model=global_config.model.focus_self_recognize,
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temperature=global_config.model.focus_self_recognize["temp"],
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max_tokens=800,
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request_type="focus_chat_mind",
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request_type="focus_self_identify",
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)
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self.current_mind = ""
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self.past_mind = []
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self.structured_info = []
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self.structured_info_str = ""
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name = chat_manager.get_stream_name(self.subheartflow_id)
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self.log_prefix = f"[{name}] "
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self._update_structured_info_str()
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def _update_structured_info_str(self):
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"""根据 structured_info 更新 structured_info_str"""
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if not self.structured_info:
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self.structured_info_str = ""
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return
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lines = ["【信息】"]
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for item in self.structured_info:
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# 简化展示，突出内容和类型，包含TTL供调试
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type_str = item.get("type", "未知类型")
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content_str = item.get("content", "")
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if type_str == "info":
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lines.append(f"刚刚: {content_str}")
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elif type_str == "memory":
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lines.append(f"{content_str}")
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elif type_str == "comparison_result":
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lines.append(f"数字大小比较结果: {content_str}")
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elif type_str == "time_info":
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lines.append(f"{content_str}")
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elif type_str == "lpmm_knowledge":
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lines.append(f"你知道：{content_str}")
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else:
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lines.append(f"{type_str}的信息: {content_str}")
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self.structured_info_str = "\n".join(lines)
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logger.debug(f"{self.log_prefix} 更新 structured_info_str: \n{self.structured_info_str}")
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async def process_info(
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self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None, *infos
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@ -125,14 +75,18 @@ class MindProcessor(BaseProcessor):
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Returns:
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List[InfoBase]: 处理后的结构化信息列表
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"""
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current_mind = await self.do_thinking_before_reply(observations, running_memorys)
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self_info_str = await self.self_indentify(observations, running_memorys)
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mind_info = MindInfo()
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mind_info.set_current_mind(current_mind)
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if self_info_str:
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self_info = SelfInfo()
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self_info.set_self_info(self_info_str)
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else:
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self_info = None
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return None
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return [mind_info]
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return [self_info]
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async def do_thinking_before_reply(
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async def self_indentify(
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self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None
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):
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"""
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@ -148,20 +102,12 @@ class MindProcessor(BaseProcessor):
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tuple: (current_mind, past_mind, prompt) 当前想法、过去的想法列表和使用的prompt
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"""
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# ---------- 0. 更新和清理 structured_info ----------
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if self.structured_info:
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updated_info = []
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for item in self.structured_info:
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item["ttl"] -= 1
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if item["ttl"] > 0:
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updated_info.append(item)
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else:
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logger.debug(f"{self.log_prefix} 移除过期的 structured_info 项: {item['id']}")
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self.structured_info = updated_info
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self._update_structured_info_str()
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logger.debug(
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f"{self.log_prefix} 当前完整的 structured_info: {safe_json_dumps(self.structured_info, ensure_ascii=False)}"
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)
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for observation in observations:
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if isinstance(observation, ChattingObservation):
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is_group_chat = observation.is_group_chat
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chat_target_info = observation.chat_target_info
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chat_target_name = "对方" # 私聊默认名称
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person_list = observation.person_list
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memory_str = ""
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if running_memorys:
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@ -169,9 +115,10 @@ class MindProcessor(BaseProcessor):
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for running_memory in running_memorys:
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memory_str += f"{running_memory['topic']}: {running_memory['content']}\n"
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# ---------- 1. 准备基础数据 ----------
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# 获取现有想法和情绪状态
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previous_mind = self.current_mind if self.current_mind else ""
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relation_prompt = ""
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for person in person_list:
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if len(person) >= 3 and person[0] and person[1]:
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relation_prompt += await relationship_manager.build_relationship_info(person, is_id=True)
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if observations is None:
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observations = []
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@ -190,52 +137,58 @@ class MindProcessor(BaseProcessor):
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chat_observe_info = observation.get_observe_info()
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person_list = observation.person_list
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if isinstance(observation, HFCloopObservation):
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hfcloop_observe_info = observation.get_observe_info()
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# hfcloop_observe_info = observation.get_observe_info()
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pass
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# ---------- 3. 准备个性化数据 ----------
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# 获取个性化信息
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nickname_str = ""
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for nicknames in global_config.bot.alias_names:
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nickname_str += f"{nicknames},"
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name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。"
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relation_prompt = ""
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personality_block = individuality.get_personality_prompt(x_person=2, level=2)
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identity_block = individuality.get_identity_prompt(x_person=2, level=2)
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if is_group_chat:
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relation_prompt_init = "在这个群聊中，你：\n"
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else:
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relation_prompt_init = ""
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for person in person_list:
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relation_prompt += await relationship_manager.build_relationship_info(person, is_id=True)
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if relation_prompt:
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relation_prompt = relation_prompt_init + relation_prompt
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else:
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relation_prompt = relation_prompt_init + "没有特别在意的人\n"
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template_name = "sub_heartflow_prompt_before" if is_group_chat else "sub_heartflow_prompt_private_before"
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logger.debug(f"{self.log_prefix} 使用{'群聊' if is_group_chat else '私聊'}思考模板")
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prompt = (await global_prompt_manager.get_prompt_async(template_name)).format(
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bot_name=individuality.name,
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prompt = (await global_prompt_manager.get_prompt_async("indentify_prompt")).format(
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name_block=name_block,
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prompt_personality=personality_block,
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indentify_block=identity_block,
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memory_str=memory_str,
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extra_info=self.structured_info_str,
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relation_prompt=relation_prompt,
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time_now=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
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chat_observe_info=chat_observe_info,
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last_mind=previous_mind,
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cycle_info_block=hfcloop_observe_info,
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chat_target_name=chat_target_name,
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)
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content = "(不知道该想些什么...)"
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# print(prompt)
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content = ""
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try:
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content, _ = await self.llm_model.generate_response_async(prompt=prompt)
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if not content:
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logger.warning(f"{self.log_prefix} LLM返回空结果，思考失败。")
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logger.warning(f"{self.log_prefix} LLM返回空结果，自我识别失败。")
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except Exception as e:
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# 处理总体异常
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logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}")
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logger.error(traceback.format_exc())
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content = "思考过程中出现错误"
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content = "自我识别过程中出现错误"
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if content == "None":
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content = ""
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# 记录初步思考结果
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logger.debug(f"{self.log_prefix} 思考prompt: \n{prompt}\n")
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logger.info(f"{self.log_prefix} 思考结果: {content}")
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self.update_current_mind(content)
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logger.debug(f"{self.log_prefix} 自我识别prompt: \n{prompt}\n")
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logger.info(f"{self.log_prefix} 自我识别结果: {content}")
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return content
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def update_current_mind(self, response):
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if self.current_mind: # 只有当 current_mind 非空时才添加到 past_mind
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self.past_mind.append(self.current_mind)
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self.current_mind = response
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init_prompt()
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