322 lines
14 KiB
Python
322 lines
14 KiB
Python
from .observation import Observation
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from src.plugins.models.utils_model import LLMRequest
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from src.config.config import global_config
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import time
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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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import random
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from ..plugins.utils.prompt_builder import Prompt, global_prompt_manager
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from src.do_tool.tool_use import ToolUser
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from src.plugins.utils.json_utils import safe_json_dumps, normalize_llm_response, process_llm_tool_calls
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from src.heart_flow.chat_state_info import ChatStateInfo
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from src.plugins.chat.chat_stream import chat_manager
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from src.plugins.heartFC_chat.heartFC_Cycleinfo import CycleInfo
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logger = get_logger("sub_heartflow")
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def init_prompt():
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prompt = ""
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prompt += "{extra_info}\n"
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prompt += "{prompt_personality}\n"
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prompt += "{last_loop_prompt}\n"
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prompt += "{cycle_info_block}\n"
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prompt += "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：\n{chat_observe_info}\n"
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prompt += "\n你现在{mood_info}\n"
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prompt += (
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"请仔细阅读当前群聊内容，分析讨论话题和群成员关系，分析你刚刚发言和别人对你的发言的反应，思考你要不要回复。"
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)
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prompt += "思考并输出你的内心想法\n"
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prompt += "输出要求：\n"
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prompt += "1. 根据聊天内容生成你的想法，{hf_do_next}\n"
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prompt += "2. 不要分点、不要使用表情符号\n"
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prompt += "3. 避免多余符号(冒号、引号、括号等)\n"
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prompt += "4. 语言简洁自然，不要浮夸\n"
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prompt += "5. 如果你刚发言，并且没有人回复你，不要回复\n"
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prompt += "工具使用说明：\n"
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prompt += "1. 输出想法后考虑是否需要使用工具\n"
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prompt += "2. 工具可获取信息或执行操作\n"
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prompt += "3. 如需处理消息或回复，请使用工具\n"
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Prompt(prompt, "sub_heartflow_prompt_before")
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prompt = ""
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prompt += "刚刚你的内心想法是：{current_thinking_info}\n"
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prompt += "{if_replan_prompt}\n"
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Prompt(prompt, "last_loop")
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class SubMind:
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def __init__(self, subheartflow_id: str, chat_state: ChatStateInfo, observations: Observation):
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self.subheartflow_id = subheartflow_id
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self.llm_model = LLMRequest(
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model=global_config.llm_sub_heartflow,
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temperature=global_config.llm_sub_heartflow["temp"],
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max_tokens=800,
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request_type="sub_heart_flow",
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)
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self.chat_state = chat_state
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self.observations = observations
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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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async def do_thinking_before_reply(self, history_cycle: list[CycleInfo] = None):
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"""
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在回复前进行思考，生成内心想法并收集工具调用结果
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返回:
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tuple: (current_mind, past_mind) 当前想法和过去的想法列表
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"""
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# 更新活跃时间
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self.last_active_time = time.time()
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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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current_thinking_info = self.current_mind
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mood_info = self.chat_state.mood
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# 获取观察对象
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observation = self.observations[0]
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if not observation:
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logger.error(f"[{self.subheartflow_id}] 无法获取观察对象")
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self.update_current_mind("(我没看到任何聊天内容...)")
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return self.current_mind, self.past_mind
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# 获取观察内容
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chat_observe_info = observation.get_observe_info()
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# ---------- 2. 准备工具和个性化数据 ----------
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# 初始化工具
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tool_instance = ToolUser()
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tools = tool_instance._define_tools()
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# 获取个性化信息
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individuality = Individuality.get_instance()
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# 构建个性部分
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prompt_personality = f"你正在扮演名为{individuality.personality.bot_nickname}的人类，你"
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prompt_personality += individuality.personality.personality_core
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# 随机添加个性侧面
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if individuality.personality.personality_sides:
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random_side = random.choice(individuality.personality.personality_sides)
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prompt_personality += f"，{random_side}"
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# 随机添加身份细节
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if individuality.identity.identity_detail:
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random_detail = random.choice(individuality.identity.identity_detail)
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prompt_personality += f"，{random_detail}"
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# 获取当前时间
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time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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# ---------- 3. 构建思考指导部分 ----------
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# 创建本地随机数生成器，基于分钟数作为种子
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local_random = random.Random()
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current_minute = int(time.strftime("%M"))
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local_random.seed(current_minute)
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# 思考指导选项和权重
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hf_options = [
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("可以参考之前的想法，在原来想法的基础上继续思考", 0.2),
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("可以参考之前的想法，在原来的想法上尝试新的话题", 0.4),
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("不要太深入", 0.2),
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("进行深入思考", 0.2),
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]
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last_cycle = history_cycle[-1] if history_cycle else None
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# 上一次决策信息
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if last_cycle != None:
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last_action = last_cycle.action_type
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last_reasoning = last_cycle.reasoning
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is_replan = last_cycle.replanned
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if is_replan:
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if_replan_prompt = f"但是你有了上述想法之后，有了新消息，你决定重新思考后，你做了：{last_action}\n因为：{last_reasoning}\n"
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else:
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if_replan_prompt = f"出于这个想法，你刚才做了：{last_action}\n因为：{last_reasoning}\n"
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else:
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last_action = ""
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last_reasoning = ""
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is_replan = False
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if_replan_prompt = ""
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if current_thinking_info:
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last_loop_prompt = (await global_prompt_manager.get_prompt_async("last_loop")).format(
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current_thinking_info=current_thinking_info, if_replan_prompt=if_replan_prompt
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)
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else:
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last_loop_prompt = ""
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# 准备循环信息块 (分析最近的活动循环)
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recent_active_cycles = []
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for cycle in reversed(history_cycle):
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# 只关心实际执行了动作的循环
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if cycle.action_taken:
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recent_active_cycles.append(cycle)
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# 最多找最近的3个活动循环
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if len(recent_active_cycles) == 3:
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break
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cycle_info_block = ""
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consecutive_text_replies = 0
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responses_for_prompt = []
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# 检查这最近的活动循环中有多少是连续的文本回复 (从最近的开始看)
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for cycle in recent_active_cycles:
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if cycle.action_type == "text_reply":
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consecutive_text_replies += 1
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# 获取回复内容，如果不存在则返回'[空回复]'
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response_text = cycle.response_info.get("response_text", [])
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# 使用简单的 join 来格式化回复内容列表
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formatted_response = "[空回复]" if not response_text else " ".join(response_text)
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responses_for_prompt.append(formatted_response)
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else:
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# 一旦遇到非文本回复，连续性中断
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break
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# 根据连续文本回复的数量构建提示信息
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# 注意: responses_for_prompt 列表是从最近到最远排序的
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if consecutive_text_replies >= 3: # 如果最近的三个活动都是文本回复
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cycle_info_block = f'你已经连续回复了三条消息（最近: "{responses_for_prompt[0]}"，第二近: "{responses_for_prompt[1]}"，第三近: "{responses_for_prompt[2]}"）。你回复的有点多了，请注意'
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elif consecutive_text_replies == 2: # 如果最近的两个活动是文本回复
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cycle_info_block = f'你已经连续回复了两条消息（最近: "{responses_for_prompt[0]}"，第二近: "{responses_for_prompt[1]}"），请注意'
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elif consecutive_text_replies == 1: # 如果最近的一个活动是文本回复
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cycle_info_block = f'你刚刚已经回复一条消息（内容: "{responses_for_prompt[0]}"）'
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# 包装提示块，增加可读性，即使没有连续回复也给个标记
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if cycle_info_block:
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cycle_info_block = f"\n【近期回复历史】\n{cycle_info_block}\n"
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else:
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# 如果最近的活动循环不是文本回复，或者没有活动循环
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cycle_info_block = "\n【近期回复历史】\n(最近没有连续文本回复)\n"
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# 加权随机选择思考指导
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hf_do_next = local_random.choices(
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[option[0] for option in hf_options], weights=[option[1] for option in hf_options], k=1
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)[0]
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# ---------- 4. 构建最终提示词 ----------
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# 获取提示词模板并填充数据
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prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_before")).format(
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extra_info="", # 可以在这里添加额外信息
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prompt_personality=prompt_personality,
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bot_name=individuality.personality.bot_nickname,
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time_now=time_now,
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chat_observe_info=chat_observe_info,
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mood_info=mood_info,
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hf_do_next=hf_do_next,
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last_loop_prompt=last_loop_prompt,
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cycle_info_block=cycle_info_block,
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)
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# logger.debug(f"[{self.subheartflow_id}] 心流思考提示词构建完成")
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# ---------- 5. 执行LLM请求并处理响应 ----------
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content = "" # 初始化内容变量
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_reasoning_content = "" # 初始化推理内容变量
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try:
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# 调用LLM生成响应
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response = await self.llm_model.generate_response_tool_async(prompt=prompt, tools=tools)
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# 标准化响应格式
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success, normalized_response, error_msg = normalize_llm_response(
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response, log_prefix=f"[{self.subheartflow_id}] "
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)
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if not success:
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# 处理标准化失败情况
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logger.warning(f"[{self.subheartflow_id}] {error_msg}")
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content = "LLM响应格式无法处理"
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else:
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# 从标准化响应中提取内容
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if len(normalized_response) >= 2:
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content = normalized_response[0]
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_reasoning_content = normalized_response[1] if len(normalized_response) > 1 else ""
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# 处理可能的工具调用
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if len(normalized_response) == 3:
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# 提取并验证工具调用
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success, valid_tool_calls, error_msg = process_llm_tool_calls(
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normalized_response, log_prefix=f"[{self.subheartflow_id}] "
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)
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if success and valid_tool_calls:
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# 记录工具调用信息
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tool_calls_str = ", ".join(
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[call.get("function", {}).get("name", "未知工具") for call in valid_tool_calls]
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)
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logger.info(
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f"[{self.subheartflow_id}] 模型请求调用{len(valid_tool_calls)}个工具: {tool_calls_str}"
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)
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# 收集工具执行结果
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await self._execute_tool_calls(valid_tool_calls, tool_instance)
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elif not success:
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logger.warning(f"[{self.subheartflow_id}] {error_msg}")
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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.subheartflow_id}] 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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# 记录最终思考结果
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name = chat_manager.get_stream_name(self.subheartflow_id)
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logger.debug(f"[{name}] \nPrompt:\n{prompt}\n\n心流思考结果:\n{content}\n")
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# 处理空响应情况
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if not content:
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content = "(不知道该想些什么...)"
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logger.warning(f"[{self.subheartflow_id}] LLM返回空结果，思考失败。")
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# ---------- 6. 更新思考状态并返回结果 ----------
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# 更新当前思考内容
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self.update_current_mind(content)
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return self.current_mind, self.past_mind
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async def _execute_tool_calls(self, tool_calls, tool_instance):
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"""
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执行一组工具调用并收集结果
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参数:
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tool_calls: 工具调用列表
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tool_instance: 工具使用器实例
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"""
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tool_results = []
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structured_info = {} # 动态生成键
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# 执行所有工具调用
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for tool_call in tool_calls:
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try:
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result = await tool_instance._execute_tool_call(tool_call)
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if result:
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tool_results.append(result)
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# 使用工具名称作为键
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tool_name = result["name"]
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if tool_name not in structured_info:
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structured_info[tool_name] = []
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structured_info[tool_name].append({"name": result["name"], "content": result["content"]})
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except Exception as tool_e:
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logger.error(f"[{self.subheartflow_id}] 工具执行失败: {tool_e}")
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# 如果有工具结果，记录并更新结构化信息
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if structured_info:
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logger.debug(f"工具调用收集到结构化信息: {safe_json_dumps(structured_info, ensure_ascii=False)}")
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self.structured_info = structured_info
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def update_current_mind(self, response):
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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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