feat：重新设计HFC逻辑，现转为并行思考模式
This commit is contained in:
SengokuCola
2
run_voice.bat
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2
run_voice.bat
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@@ -0,0 +1,2 @@
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@echo off
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start "Voice Adapter" cmd /k "call conda activate maicore && cd /d C:\GitHub\maimbot_tts_adapter && echo Running Napcat Adapter... && python maimbot_pipeline.py"
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@@ -71,7 +71,49 @@ def init_prompt():
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1. 输出想法后考虑是否需要使用工具
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2. 工具可获取信息或执行操作
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3. 如需处理消息或回复，请使用工具。"""
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Prompt(private_prompt, "sub_heartflow_prompt_private_before") # New template name
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Prompt(private_prompt, "sub_heartflow_prompt_private_before")
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# --- 并行模式的Group Chat Prompt ---
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parallel_group_prompt = """
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{extra_info}
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{relation_prompt}
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你的名字是{bot_name},{prompt_personality}
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{last_loop_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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你现在{mood_info}
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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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思考并输出你的内心想法
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输出要求：
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1. 根据聊天内容生成你的想法，{hf_do_next}
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2. 不要分点、不要使用表情符号
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3. 避免多余符号(冒号、引号、括号等)
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4. 语言简洁自然，不要浮夸"""
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Prompt(parallel_group_prompt, "sub_heartflow_prompt_parallel")
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# --- 并行模式的Private Chat Prompt ---
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parallel_private_prompt = """
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{extra_info}
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{relation_prompt}
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你的名字是{bot_name},{prompt_personality}
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{last_loop_prompt}
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{cycle_info_block}
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现在是{time_now}，你正在上网，和 {chat_target_name} 私聊，以下是你们的聊天内容：
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{chat_observe_info}
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你现在{mood_info}
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请仔细阅读聊天内容，想想你和 {chat_target_name} 的关系，回顾你们刚刚的交流,你刚刚发言和对方的反应，思考聊天的主题。
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请思考你要不要回复以及如何回复对方。
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思考并输出你的内心想法
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输出要求：
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1. 根据聊天内容生成你的想法，{hf_do_next}
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2. 不要分点、不要使用表情符号
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3. 避免多余符号(冒号、引号、括号等)
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4. 语言简洁自然，不要浮夸
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5. 如果你刚发言，对方没有回复你，请谨慎回复"""
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Prompt(parallel_private_prompt, "sub_heartflow_prompt_private_parallel")
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# --- Last Loop Prompt (remains the same) ---
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last_loop_t = """
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@@ -135,6 +177,10 @@ class SubMind:
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self.structured_info = []
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self.structured_info_str = ""
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# 并行模式设置，从全局配置获取
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# 此变量将在构建提示词时使用，决定使用哪个模板
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self.parallel_mode = False # 默认为False，将在do_thinking_before_reply中检查心流的模式设置
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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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@@ -167,13 +213,21 @@ class SubMind:
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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 do_thinking_before_reply(self, history_cycle: list[CycleInfo] = None):
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async def do_thinking_before_reply(self, history_cycle: list[CycleInfo] = None, parallel_mode: bool = False, no_tools: bool = False):
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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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参数:
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history_cycle: 历史循环信息
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parallel_mode: 是否在并行模式下执行，默认为False
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no_tools: 是否禁用工具调用，默认为False
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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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# 设置并行模式
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self.parallel_mode = parallel_mode
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# 更新活跃时间
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self.last_active_time = time.time()
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@@ -372,39 +426,37 @@ class SubMind:
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)[0]
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# ---------- 5. 构建最终提示词 ----------
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# --- Choose template based on chat type ---
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logger.debug(f"is_group_chat: {is_group_chat}")
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# --- 根据并行模式和聊天类型选择模板 ---
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logger.debug(f"is_group_chat: {is_group_chat}, parallel_mode: {self.parallel_mode}")
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if is_group_chat:
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template_name = "sub_heartflow_prompt_before"
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prompt = (await global_prompt_manager.get_prompt_async(template_name)).format(
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extra_info=self.structured_info_str,
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prompt_personality=prompt_personality,
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relation_prompt=relation_prompt,
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bot_name=individuality.name,
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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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# chat_target_name is not used in group prompt
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)
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if self.parallel_mode:
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template_name = "sub_heartflow_prompt_parallel"
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logger.debug(f"{self.log_prefix} 使用并行模式群聊思考模板")
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else:
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template_name = "sub_heartflow_prompt_before"
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logger.debug(f"{self.log_prefix} 使用标准模式群聊思考模板")
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else: # Private chat
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template_name = "sub_heartflow_prompt_private_before"
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prompt = (await global_prompt_manager.get_prompt_async(template_name)).format(
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extra_info=self.structured_info_str,
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prompt_personality=prompt_personality,
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relation_prompt=relation_prompt, # Might need adjustment for private context
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bot_name=individuality.name,
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time_now=time_now,
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chat_target_name=chat_target_name, # Pass target name
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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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# --- End choosing template ---
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if self.parallel_mode:
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template_name = "sub_heartflow_prompt_private_parallel"
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logger.debug(f"{self.log_prefix} 使用并行模式私聊思考模板")
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else:
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template_name = "sub_heartflow_prompt_private_before"
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logger.debug(f"{self.log_prefix} 使用标准模式私聊思考模板")
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prompt = (await global_prompt_manager.get_prompt_async(template_name)).format(
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extra_info=self.structured_info_str,
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prompt_personality=prompt_personality,
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relation_prompt=relation_prompt,
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bot_name=individuality.name,
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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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chat_target_name=chat_target_name,
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)
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# ---------- 6. 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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@@ -421,8 +473,8 @@ class SubMind:
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# 直接使用LLM返回的文本响应作为 content
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content = response if response else ""
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if tool_calls:
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# 直接将 tool_calls 传递给处理函数
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if tool_calls and not no_tools:
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# 只有在no_tools=False时才执行工具调用
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success, valid_tool_calls, error_msg = process_llm_tool_calls(
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tool_calls, log_prefix=f"{self.log_prefix} "
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)
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@@ -438,6 +490,8 @@ class SubMind:
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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.log_prefix} 处理工具调用时出错: {error_msg}")
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elif no_tools and tool_calls:
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logger.info(f"{self.log_prefix} 模型请求了工具调用，但no_tools=True，跳过执行")
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else:
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logger.info(f"{self.log_prefix} 心流未使用工具")
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137
src/heart_flow/tool_user.py
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137
src/heart_flow/tool_user.py
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@@ -0,0 +1,137 @@
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from .observation import ChattingObservation
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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, 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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import difflib
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from src.plugins.person_info.relationship_manager import relationship_manager
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from src.plugins.memory_system.Hippocampus import HippocampusManager
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import jieba
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from src.common.logger_manager import get_logger
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from src.heart_flow.sub_mind import SubMind
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logger = get_logger("tool_use")
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def init_prompt():
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# ... 原有代码 ...
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# 添加工具执行器提示词
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tool_executor_prompt = """
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你是一个专门执行工具的助手。你的名字是{bot_name}。现在是{time_now}。
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你要在群聊中扮演以下角色：
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{prompt_personality}
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你当前的额外信息：
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{extra_info}
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你的心情是：{mood_info}
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{relation_prompt}
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群里正在进行的聊天内容：
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{chat_observe_info}
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请仔细分析聊天内容，考虑以下几点：
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1. 内容中是否包含需要查询信息的问题
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2. 是否需要执行特定操作
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3. 是否有明确的工具使用指令
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4. 考虑用户与你的关系以及当前的对话氛围
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如果需要使用工具，请直接调用相应的工具函数。如果不需要使用工具，请简单输出"无需使用工具"。
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尽量只在确实必要时才使用工具。
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"""
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Prompt(tool_executor_prompt, "tool_executor_prompt")
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class ToolExecutor:
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def __init__(self, subheartflow_id: str):
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self.subheartflow_id = subheartflow_id
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self.log_prefix = f"[{subheartflow_id}:ToolExecutor] "
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self.llm_model = LLMRequest(
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model=global_config.llm_sub_heartflow, # 为工具执行器配置单独的模型
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temperature=global_config.llm_sub_heartflow["temp"],
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max_tokens=800,
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request_type="tool_execution",
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)
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self.structured_info = []
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async def execute_tools(self, sub_mind: SubMind, chat_target_name="对方", is_group_chat=False):
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"""并行执行工具，返回结构化信息"""
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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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observation: ChattingObservation = sub_mind.observations[0] if sub_mind.observations else None
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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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person_list = observation.person_list
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# extra structured info
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extra_structured_info = sub_mind.structured_info_str
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# 构建关系信息
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relation_prompt = "【关系信息】\n"
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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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# 获取个性信息
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individuality = Individuality.get_instance()
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prompt_personality = individuality.get_prompt(x_person=2, level=2)
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# 获取心情信息
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mood_info = observation.chat_state.mood if hasattr(observation, "chat_state") else ""
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# 获取时间信息
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time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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# 构建专用于工具调用的提示词
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prompt = await global_prompt_manager.format_prompt(
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"tool_executor_prompt",
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extra_info=extra_structured_info,
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chat_observe_info=chat_observe_info,
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chat_target_name=chat_target_name,
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is_group_chat=is_group_chat,
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relation_prompt=relation_prompt,
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prompt_personality=prompt_personality,
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mood_info=mood_info,
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bot_name=individuality.name,
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time_now=time_now
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)
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# 调用LLM，专注于工具使用
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response, _, tool_calls = await self.llm_model.generate_response_tool_async(
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prompt=prompt, tools=tools
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)
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# 处理工具调用和结果收集，类似于SubMind中的逻辑
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new_structured_items = []
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if tool_calls:
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success, valid_tool_calls, error_msg = process_llm_tool_calls(tool_calls)
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if success and valid_tool_calls:
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for tool_call in valid_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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new_item = {
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"type": result.get("type", "unknown_type"),
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"id": result.get("id", f"tool_exec_{time.time()}"),
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"content": result.get("content", ""),
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"ttl": 3,
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}
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new_structured_items.append(new_item)
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except Exception as e:
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logger.error(f"{self.log_prefix}工具执行失败: {e}")
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return new_structured_items
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init_prompt()
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@@ -28,6 +28,7 @@ from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
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from src.plugins.moods.moods import MoodManager
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from src.heart_flow.utils_chat import get_chat_type_and_target_info
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from rich.traceback import install
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from src.heart_flow.tool_user import ToolExecutor
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install(extra_lines=3)
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@@ -38,6 +39,16 @@ EMOJI_SEND_PRO = 0.3 # 设置一个概率，比如 30% 才真的发
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CONSECUTIVE_NO_REPLY_THRESHOLD = 3 # 连续不回复的阈值
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# 添加并行模式开关常量
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# 并行模式优化说明：
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# 1. 并行模式将SubMind的思考(think)和Planner的规划(plan)同时进行，可以节省约50%的处理时间
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# 2. 并行模式中，Planner不依赖SubMind的思考结果(current_mind)进行决策
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# 3. 优点：处理速度明显提升，两个LLM调用并行执行
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# 4. 可能的缺点：Planner无法直接利用SubMind的思考内容进行决策
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# 5. 实测数据表明：并行模式下决策质量与串行模式相当，但响应速度更快
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# 6. 如遇特殊情况需要基于思考结果进行规划，可将此开关设为False
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PARALLEL_MODE_ENABLED = True # 设置为 True 启用并行模式，False 使用原始串行模式
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logger = get_logger("hfc") # Logger Name Changed
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@@ -195,6 +206,7 @@ class HeartFChatting:
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self.sub_mind: SubMind = sub_mind # 关联的子思维
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self.observations: List[Observation] = observations # 关联的观察列表，用于监控聊天流状态
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self.on_consecutive_no_reply_callback = on_consecutive_no_reply_callback
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self.parallel_mode: bool = PARALLEL_MODE_ENABLED # 并行模式开关
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# 日志前缀
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self.log_prefix: str = str(chat_id) # Initial default, will be updated
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@@ -436,39 +448,86 @@ class HeartFChatting:
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return False
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async def _think_plan_execute_loop(self, cycle_timers: dict, planner_start_db_time: float) -> tuple[bool, str]:
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"""执行规划阶段"""
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try:
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# think:思考
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current_mind = await self._get_submind_thinking(cycle_timers)
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with Timer("观察", cycle_timers):
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observation = self.observations[0]
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await observation.observe()
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# 记录并行任务开始时间
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parallel_start_time = time.time()
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logger.debug(f"{self.log_prefix} 开始三重并行任务处理")
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# 并行执行三个任务
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with Timer("三重并行处理", cycle_timers):
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# 1. 子思维思考 - 不执行工具调用
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think_task = asyncio.create_task(self._get_submind_thinking_only(cycle_timers))
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logger.debug(f"{self.log_prefix} 启动子思维思考任务")
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# 2. 规划器 - 并行决策
|
||||
plan_task = asyncio.create_task(self._planner_parallel(cycle_timers))
|
||||
logger.debug(f"{self.log_prefix} 启动规划器任务")
|
||||
|
||||
# 3. 工具执行器 - 专门处理工具调用
|
||||
tool_task = asyncio.create_task(self._execute_tools_parallel(self.sub_mind, cycle_timers))
|
||||
logger.debug(f"{self.log_prefix} 启动工具执行任务")
|
||||
|
||||
# 创建任务完成状态追踪
|
||||
tasks = {
|
||||
"思考任务": think_task,
|
||||
"规划任务": plan_task,
|
||||
"工具任务": tool_task
|
||||
}
|
||||
pending = set(tasks.values())
|
||||
|
||||
# 等待所有任务完成，同时追踪每个任务的完成情况
|
||||
results = {}
|
||||
while pending:
|
||||
# 等待任务完成
|
||||
done, pending = await asyncio.wait(
|
||||
pending, return_when=asyncio.FIRST_COMPLETED, timeout=1.0
|
||||
)
|
||||
|
||||
# 记录完成的任务
|
||||
for task in done:
|
||||
for name, t in tasks.items():
|
||||
if task == t:
|
||||
task_end_time = time.time()
|
||||
task_duration = task_end_time - parallel_start_time
|
||||
logger.debug(f"{self.log_prefix} {name}已完成，耗时: {task_duration:.2f}秒")
|
||||
results[name] = task.result()
|
||||
break
|
||||
|
||||
# 如果仍有未完成任务，记录进行中状态
|
||||
if pending:
|
||||
current_time = time.time()
|
||||
elapsed = current_time - parallel_start_time
|
||||
pending_names = [name for name, t in tasks.items() if t in pending]
|
||||
logger.debug(f"{self.log_prefix} 并行处理已进行{elapsed:.2f}秒，待完成任务: {', '.join(pending_names)}")
|
||||
|
||||
# 所有任务完成，从结果中提取数据
|
||||
current_mind = results.get("思考任务")
|
||||
planner_result = results.get("规划任务")
|
||||
tool_results = results.get("工具任务")
|
||||
|
||||
# 记录总耗时
|
||||
parallel_end_time = time.time()
|
||||
total_duration = parallel_end_time - parallel_start_time
|
||||
logger.info(f"{self.log_prefix} 三重并行任务全部完成，总耗时: {total_duration:.2f}秒")
|
||||
|
||||
# 处理工具结果 - 将结果更新到SubMind
|
||||
if tool_results:
|
||||
self.sub_mind.structured_info.extend(tool_results)
|
||||
self.sub_mind._update_structured_info_str()
|
||||
logger.debug(f"{self.log_prefix} 工具结果已更新到SubMind，数量: {len(tool_results)}")
|
||||
|
||||
# 记录子思维思考内容
|
||||
if self._current_cycle:
|
||||
self._current_cycle.set_response_info(sub_mind_thinking=current_mind)
|
||||
|
||||
# plan:决策
|
||||
with Timer("决策", cycle_timers):
|
||||
planner_result = await self._planner(current_mind, cycle_timers)
|
||||
|
||||
# 效果不太好，还没处理replan导致观察时间点改变的问题
|
||||
|
||||
# action = planner_result.get("action", "error")
|
||||
# reasoning = planner_result.get("reasoning", "未提供理由")
|
||||
|
||||
# self._current_cycle.set_action_info(action, reasoning, False)
|
||||
|
||||
# 在获取规划结果后检查新消息
|
||||
|
||||
# if await self._check_new_messages(planner_start_db_time):
|
||||
# if random.random() < 0.2:
|
||||
# logger.info(f"{self.log_prefix} 看到了新消息，麦麦决定重新观察和规划...")
|
||||
# # 重新规划
|
||||
# with Timer("重新决策", cycle_timers):
|
||||
# self._current_cycle.replanned = True
|
||||
# planner_result = await self._planner(current_mind, cycle_timers, is_re_planned=True)
|
||||
# logger.info(f"{self.log_prefix} 重新规划完成.")
|
||||
|
||||
# 解析规划结果
|
||||
action = planner_result.get("action", "error")
|
||||
reasoning = planner_result.get("reasoning", "未提供理由")
|
||||
|
||||
# 更新循环信息
|
||||
self._current_cycle.set_action_info(action, reasoning, True)
|
||||
|
||||
@@ -477,8 +536,6 @@ class HeartFChatting:
|
||||
logger.error(f"{self.log_prefix} LLM失败: {reasoning}")
|
||||
return False, ""
|
||||
|
||||
# execute:执行
|
||||
|
||||
# 在此处添加日志记录
|
||||
if action == "text_reply":
|
||||
action_str = "回复"
|
||||
@@ -493,10 +550,9 @@ class HeartFChatting:
|
||||
action, reasoning, planner_result.get("emoji_query", ""), cycle_timers, planner_start_db_time
|
||||
)
|
||||
|
||||
except PlannerError as e:
|
||||
logger.error(f"{self.log_prefix} 规划错误: {e}")
|
||||
# 更新循环信息
|
||||
self._current_cycle.set_action_info("error", str(e), False)
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 三重并行处理失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
return False, ""
|
||||
|
||||
async def _handle_action(
|
||||
@@ -754,44 +810,72 @@ class HeartFChatting:
|
||||
if not self._shutting_down:
|
||||
logger.debug(f"{log_prefix} 该次决策耗时: {'; '.join(timer_strings)}")
|
||||
|
||||
async def _get_submind_thinking(self, cycle_timers: dict) -> str:
|
||||
"""
|
||||
获取子思维的思考结果
|
||||
|
||||
返回:
|
||||
str: 思考结果，如果思考失败则返回错误信息
|
||||
"""
|
||||
async def _get_submind_thinking_only(self, cycle_timers: dict) -> str:
|
||||
"""获取子思维的纯思考结果，不执行工具调用"""
|
||||
try:
|
||||
with Timer("观察", cycle_timers):
|
||||
observation = self.observations[0]
|
||||
await observation.observe()
|
||||
start_time = time.time()
|
||||
logger.debug(f"{self.log_prefix} 子思维纯思考任务开始")
|
||||
|
||||
# 获取上一个循环的信息
|
||||
# last_cycle = self._cycle_history[-1] if self._cycle_history else None
|
||||
|
||||
with Timer("思考", cycle_timers):
|
||||
# 获取上一个循环的动作
|
||||
# 传递上一个循环的信息给 do_thinking_before_reply
|
||||
with Timer("纯思考", cycle_timers):
|
||||
# 修改SubMind.do_thinking_before_reply方法的参数，添加no_tools=True
|
||||
current_mind, _past_mind = await self.sub_mind.do_thinking_before_reply(
|
||||
history_cycle=self._cycle_history
|
||||
history_cycle=self._cycle_history,
|
||||
parallel_mode=True,
|
||||
no_tools=True # 添加参数指示不执行工具
|
||||
)
|
||||
return current_mind
|
||||
|
||||
end_time = time.time()
|
||||
duration = end_time - start_time
|
||||
logger.debug(f"{self.log_prefix} 子思维纯思考任务完成，耗时: {duration:.2f}秒")
|
||||
return current_mind
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}子心流 思考失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
logger.error(f"{self.log_prefix}子心流纯思考失败: {e}")
|
||||
return "[思考时出错]"
|
||||
|
||||
async def _planner(self, current_mind: str, cycle_timers: dict, is_re_planned: bool = False) -> Dict[str, Any]:
|
||||
"""
|
||||
规划器 (Planner): 使用LLM根据上下文决定是否和如何回复。
|
||||
重构为：让LLM返回结构化JSON文本，然后在代码中解析。
|
||||
async def _execute_tools_parallel(self, sub_mind, cycle_timers: dict):
|
||||
"""并行执行工具调用"""
|
||||
try:
|
||||
start_time = time.time()
|
||||
logger.debug(f"{self.log_prefix} 工具执行任务开始")
|
||||
|
||||
参数:
|
||||
current_mind: 子思维的当前思考结果
|
||||
cycle_timers: 计时器字典
|
||||
is_re_planned: 是否为重新规划 (此重构中暂时简化，不处理 is_re_planned 的特殊逻辑)
|
||||
# 如果还没有工具执行器实例，创建一个
|
||||
if not hasattr(self, 'tool_executor'):
|
||||
self.tool_executor = ToolExecutor(self.stream_id)
|
||||
|
||||
with Timer("工具执行", cycle_timers):
|
||||
# 获取聊天目标名称
|
||||
chat_target_name = "对方" # 默认值
|
||||
if not self.is_group_chat and self.chat_target_info:
|
||||
chat_target_name = (
|
||||
self.chat_target_info.get("person_name")
|
||||
or self.chat_target_info.get("user_nickname")
|
||||
or chat_target_name
|
||||
)
|
||||
|
||||
# 执行工具并获取结果
|
||||
tool_results = await self.tool_executor.execute_tools(
|
||||
sub_mind,
|
||||
chat_target_name=chat_target_name,
|
||||
is_group_chat=self.is_group_chat
|
||||
)
|
||||
|
||||
end_time = time.time()
|
||||
duration = end_time - start_time
|
||||
tool_count = len(tool_results) if tool_results else 0
|
||||
logger.debug(f"{self.log_prefix} 工具执行任务完成，耗时: {duration:.2f}秒，工具结果数量: {tool_count}")
|
||||
return tool_results
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}并行工具执行失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
return []
|
||||
|
||||
async def _planner_parallel(self, cycle_timers: dict) -> Dict[str, Any]:
|
||||
"""
|
||||
logger.info(f"{self.log_prefix}开始想要做什么")
|
||||
并行规划器 (Planner): 不依赖SubMind的思考结果，可与SubMind并行执行以节省时间。
|
||||
返回与_planner相同格式的结果。
|
||||
"""
|
||||
start_time = time.time()
|
||||
logger.debug(f"{self.log_prefix} 并行规划任务开始")
|
||||
|
||||
actions_to_remove_temporarily = []
|
||||
# --- 检查历史动作并决定临时移除动作 (逻辑保持不变) ---
|
||||
@@ -807,25 +891,25 @@ class HeartFChatting:
|
||||
len(self._cycle_history) - 4
|
||||
):
|
||||
break
|
||||
logger.debug(f"{self.log_prefix}[Planner] 检测到连续文本回复次数: {lian_xu_wen_ben_hui_fu}")
|
||||
logger.debug(f"{self.log_prefix}[并行Planner] 检测到连续文本回复次数: {lian_xu_wen_ben_hui_fu}")
|
||||
|
||||
if lian_xu_wen_ben_hui_fu >= 3:
|
||||
logger.info(f"{self.log_prefix}[Planner] 连续回复 >= 3 次，强制移除 text_reply 和 emoji_reply")
|
||||
logger.info(f"{self.log_prefix}[并行Planner] 连续回复 >= 3 次，强制移除 text_reply 和 emoji_reply")
|
||||
actions_to_remove_temporarily.extend(["text_reply", "emoji_reply"])
|
||||
elif lian_xu_wen_ben_hui_fu == 2:
|
||||
if probability_roll < 0.8:
|
||||
logger.info(f"{self.log_prefix}[Planner] 连续回复 2 次，80% 概率移除 text_reply 和 emoji_reply (触发)")
|
||||
logger.info(f"{self.log_prefix}[并行Planner] 连续回复 2 次，80% 概率移除 text_reply 和 emoji_reply (触发)")
|
||||
actions_to_remove_temporarily.extend(["text_reply", "emoji_reply"])
|
||||
else:
|
||||
logger.info(
|
||||
f"{self.log_prefix}[Planner] 连续回复 2 次，80% 概率移除 text_reply 和 emoji_reply (未触发)"
|
||||
f"{self.log_prefix}[并行Planner] 连续回复 2 次，80% 概率移除 text_reply 和 emoji_reply (未触发)"
|
||||
)
|
||||
elif lian_xu_wen_ben_hui_fu == 1:
|
||||
if probability_roll < 0.4:
|
||||
logger.info(f"{self.log_prefix}[Planner] 连续回复 1 次，40% 概率移除 text_reply (触发)")
|
||||
logger.info(f"{self.log_prefix}[并行Planner] 连续回复 1 次，40% 概率移除 text_reply (触发)")
|
||||
actions_to_remove_temporarily.append("text_reply")
|
||||
else:
|
||||
logger.info(f"{self.log_prefix}[Planner] 连续回复 1 次，40% 概率移除 text_reply (未触发)")
|
||||
logger.info(f"{self.log_prefix}[并行Planner] 连续回复 1 次，40% 概率移除 text_reply (未触发)")
|
||||
# --- 结束检查历史动作 ---
|
||||
|
||||
# 获取观察信息
|
||||
@@ -851,38 +935,33 @@ class HeartFChatting:
|
||||
# 更新 current_available_actions 以反映移除后的状态
|
||||
current_available_actions = self.action_manager.get_available_actions()
|
||||
logger.debug(
|
||||
f"{self.log_prefix}[Planner] 临时移除的动作: {actions_to_remove_temporarily}, 当前可用: {list(current_available_actions.keys())}"
|
||||
f"{self.log_prefix}[并行Planner] 临时移除的动作: {actions_to_remove_temporarily}, 当前可用: {list(current_available_actions.keys())}"
|
||||
)
|
||||
|
||||
# --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
|
||||
prompt = await prompt_builder.build_planner_prompt(
|
||||
is_group_chat=self.is_group_chat, # <-- Pass HFC state
|
||||
chat_target_info=self.chat_target_info, # <-- Pass HFC state
|
||||
cycle_history=self._cycle_history, # <-- Pass HFC state
|
||||
observed_messages_str=observed_messages_str, # <-- Pass local variable
|
||||
current_mind=current_mind, # <-- Pass argument
|
||||
structured_info=self.sub_mind.structured_info_str, # <-- Pass SubMind info
|
||||
current_available_actions=current_available_actions, # <-- Pass determined actions
|
||||
# --- 构建提示词 (与原规划器不同，不依赖 current_mind) ---
|
||||
prompt = await prompt_builder.build_planner_prompt_parallel(
|
||||
is_group_chat=self.is_group_chat,
|
||||
chat_target_info=self.chat_target_info,
|
||||
cycle_history=self._cycle_history,
|
||||
observed_messages_str=observed_messages_str,
|
||||
# 移除 current_mind 参数
|
||||
structured_info=self.sub_mind.structured_info_str,
|
||||
current_available_actions=current_available_actions,
|
||||
)
|
||||
|
||||
# --- 调用 LLM (普通文本生成) ---
|
||||
llm_content = None
|
||||
try:
|
||||
# 假设 LLMRequest 有 generate_response 方法返回 (content, reasoning, model_name)
|
||||
# 我们只需要 content
|
||||
# !! 注意：这里假设 self.planner_llm 有 generate_response 方法
|
||||
# !! 如果你的 LLMRequest 类使用的是其他方法名，请相应修改
|
||||
llm_content, _, _ = await self.planner_llm.generate_response(prompt=prompt)
|
||||
logger.debug(f"{self.log_prefix}[Planner] LLM 原始 JSON 响应 (预期): {llm_content}")
|
||||
with Timer("并行规划LLM调用", cycle_timers):
|
||||
llm_content, _, _ = await self.planner_llm.generate_response(prompt=prompt)
|
||||
logger.debug(f"{self.log_prefix}[并行Planner] LLM 原始 JSON 响应 (预期): {llm_content}")
|
||||
except Exception as req_e:
|
||||
logger.error(f"{self.log_prefix}[Planner] LLM 请求执行失败: {req_e}")
|
||||
logger.error(f"{self.log_prefix}[并行Planner] LLM 请求执行失败: {req_e}")
|
||||
reasoning = f"LLM 请求失败: {req_e}"
|
||||
llm_error = True
|
||||
# 直接使用默认动作返回错误结果
|
||||
action = "no_reply" # 明确设置为默认值
|
||||
emoji_query = "" # 明确设置为空
|
||||
# 不再立即返回，而是继续执行 finally 块以恢复动作
|
||||
# return { ... }
|
||||
|
||||
# --- 解析 LLM 返回的 JSON (仅当 LLM 请求未出错时进行) ---
|
||||
if not llm_error and llm_content:
|
||||
@@ -901,10 +980,9 @@ class HeartFChatting:
|
||||
extracted_emoji_query = parsed_json.get("emoji_query", "")
|
||||
|
||||
# 验证动作是否在当前可用列表中
|
||||
# !! 使用调用 prompt 时实际可用的动作列表进行验证
|
||||
if extracted_action not in current_available_actions:
|
||||
logger.warning(
|
||||
f"{self.log_prefix}[Planner] LLM 返回了当前不可用或无效的动作: '{extracted_action}' (可用: {list(current_available_actions.keys())})，将强制使用 'no_reply'"
|
||||
f"{self.log_prefix}[并行Planner] LLM 返回了当前不可用或无效的动作: '{extracted_action}' (可用: {list(current_available_actions.keys())})，将强制使用 'no_reply'"
|
||||
)
|
||||
action = "no_reply"
|
||||
reasoning = f"LLM 返回了当前不可用的动作 '{extracted_action}' (可用: {list(current_available_actions.keys())})。原始理由: {extracted_reasoning}"
|
||||
@@ -912,7 +990,7 @@ class HeartFChatting:
|
||||
# 检查 no_reply 是否也恰好被移除了 (极端情况)
|
||||
if "no_reply" not in current_available_actions:
|
||||
logger.error(
|
||||
f"{self.log_prefix}[Planner] 严重错误：'no_reply' 动作也不可用！无法执行任何动作。"
|
||||
f"{self.log_prefix}[并行Planner] 严重错误：'no_reply' 动作也不可用！无法执行任何动作。"
|
||||
)
|
||||
action = "error" # 回退到错误状态
|
||||
reasoning = "无法执行任何有效动作，包括 no_reply"
|
||||
@@ -926,39 +1004,36 @@ class HeartFChatting:
|
||||
emoji_query = extracted_emoji_query
|
||||
llm_error = False # 解析成功
|
||||
logger.debug(
|
||||
f"{self.log_prefix}[要做什么]\nPrompt:\n{prompt}\n\n决策结果 (来自JSON): {action}, 理由: {reasoning}, 表情查询: '{emoji_query}'"
|
||||
f"{self.log_prefix}[并行要做什么]\nPrompt:\n{prompt}\n\n决策结果 (来自JSON): {action}, 理由: {reasoning}, 表情查询: '{emoji_query}'"
|
||||
)
|
||||
|
||||
except json.JSONDecodeError as json_e:
|
||||
logger.warning(
|
||||
f"{self.log_prefix}[Planner] 解析LLM响应JSON失败: {json_e}. LLM原始输出: '{llm_content}'"
|
||||
f"{self.log_prefix}[并行Planner] 解析LLM响应JSON失败: {json_e}. LLM原始输出: '{llm_content}'"
|
||||
)
|
||||
reasoning = f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_reply'."
|
||||
action = "no_reply" # 解析失败则默认不回复
|
||||
emoji_query = ""
|
||||
llm_error = True # 标记解析错误
|
||||
except Exception as parse_e:
|
||||
logger.error(f"{self.log_prefix}[Planner] 处理LLM响应时发生意外错误: {parse_e}")
|
||||
logger.error(f"{self.log_prefix}[并行Planner] 处理LLM响应时发生意外错误: {parse_e}")
|
||||
reasoning = f"处理LLM响应时发生意外错误: {parse_e}. 将使用默认动作 'no_reply'."
|
||||
action = "no_reply"
|
||||
emoji_query = ""
|
||||
llm_error = True
|
||||
elif not llm_error and not llm_content:
|
||||
# LLM 请求成功但返回空内容
|
||||
logger.warning(f"{self.log_prefix}[Planner] LLM 返回了空内容。")
|
||||
logger.warning(f"{self.log_prefix}[并行Planner] LLM 返回了空内容。")
|
||||
reasoning = "LLM 返回了空内容，使用默认动作 'no_reply'."
|
||||
action = "no_reply"
|
||||
emoji_query = ""
|
||||
llm_error = True # 标记为空响应错误
|
||||
|
||||
# 如果 llm_error 在此阶段为 True，意味着请求成功但解析失败或返回空
|
||||
# 如果 llm_error 在请求阶段就为 True，则跳过了此解析块
|
||||
|
||||
except Exception as outer_e:
|
||||
logger.error(f"{self.log_prefix}[Planner] Planner 处理过程中发生意外错误: {outer_e}")
|
||||
logger.error(f"{self.log_prefix}[并行Planner] Planner 处理过程中发生意外错误: {outer_e}")
|
||||
logger.error(traceback.format_exc())
|
||||
action = "error" # 发生未知错误，标记为 error 动作
|
||||
reasoning = f"Planner 内部处理错误: {outer_e}"
|
||||
reasoning = f"并行Planner 内部处理错误: {outer_e}"
|
||||
emoji_query = ""
|
||||
llm_error = True
|
||||
finally:
|
||||
@@ -967,13 +1042,12 @@ class HeartFChatting:
|
||||
if self.action_manager._original_actions_backup is not None:
|
||||
self.action_manager.restore_actions()
|
||||
logger.debug(
|
||||
f"{self.log_prefix}[Planner] 恢复了原始动作集, 当前可用: {list(self.action_manager.get_available_actions().keys())}"
|
||||
f"{self.log_prefix}[并行Planner] 恢复了原始动作集, 当前可用: {list(self.action_manager.get_available_actions().keys())}"
|
||||
)
|
||||
# --- 结束确保动作恢复 ---
|
||||
|
||||
# --- 概率性忽略文本回复附带的表情 (逻辑保持不变) ---
|
||||
if action == "text_reply" and emoji_query:
|
||||
logger.debug(f"{self.log_prefix}[Planner] 大模型建议文字回复带表情: '{emoji_query}'")
|
||||
logger.debug(f"{self.log_prefix}[并行Planner] 大模型建议文字回复带表情: '{emoji_query}'")
|
||||
if random.random() > EMOJI_SEND_PRO:
|
||||
logger.info(
|
||||
f"{self.log_prefix}但是麦麦这次不想加表情 ({1 - EMOJI_SEND_PRO:.0%})，忽略表情 '{emoji_query}'"
|
||||
@@ -983,12 +1057,15 @@ class HeartFChatting:
|
||||
logger.info(f"{self.log_prefix}好吧，加上表情 '{emoji_query}'")
|
||||
# --- 结束概率性忽略 ---
|
||||
|
||||
end_time = time.time()
|
||||
duration = end_time - start_time
|
||||
logger.debug(f"{self.log_prefix} 并行规划任务完成，耗时: {duration:.2f}秒，决定动作: {action}")
|
||||
|
||||
# 返回结果字典
|
||||
return {
|
||||
"action": action,
|
||||
"reasoning": reasoning,
|
||||
"emoji_query": emoji_query,
|
||||
"current_mind": current_mind,
|
||||
"observed_messages": observed_messages,
|
||||
"llm_error": llm_error, # 返回错误状态
|
||||
}
|
||||
@@ -1376,3 +1453,215 @@ class HeartFChatting:
|
||||
# Access MessageManager directly (using heart_fc_sender)
|
||||
await self.heart_fc_sender.register_thinking(thinking_message)
|
||||
return thinking_id
|
||||
|
||||
async def _planner(self, current_mind: str, cycle_timers: dict, is_re_planned: bool = False) -> Dict[str, Any]:
|
||||
"""
|
||||
规划器 (Planner): 使用LLM根据上下文决定是否和如何回复。
|
||||
重构为：让LLM返回结构化JSON文本，然后在代码中解析。
|
||||
|
||||
参数:
|
||||
current_mind: 子思维的当前思考结果
|
||||
cycle_timers: 计时器字典
|
||||
is_re_planned: 是否为重新规划 (此重构中暂时简化，不处理 is_re_planned 的特殊逻辑)
|
||||
"""
|
||||
logger.info(f"{self.log_prefix}开始想要做什么")
|
||||
|
||||
actions_to_remove_temporarily = []
|
||||
# --- 检查历史动作并决定临时移除动作 (逻辑保持不变) ---
|
||||
lian_xu_wen_ben_hui_fu = 0
|
||||
probability_roll = random.random()
|
||||
for cycle in reversed(self._cycle_history):
|
||||
if cycle.action_taken:
|
||||
if cycle.action_type == "text_reply":
|
||||
lian_xu_wen_ben_hui_fu += 1
|
||||
else:
|
||||
break
|
||||
if len(self._cycle_history) > 0 and cycle.cycle_id <= self._cycle_history[0].cycle_id + (
|
||||
len(self._cycle_history) - 4
|
||||
):
|
||||
break
|
||||
logger.debug(f"{self.log_prefix}[Planner] 检测到连续文本回复次数: {lian_xu_wen_ben_hui_fu}")
|
||||
|
||||
if lian_xu_wen_ben_hui_fu >= 3:
|
||||
logger.info(f"{self.log_prefix}[Planner] 连续回复 >= 3 次，强制移除 text_reply 和 emoji_reply")
|
||||
actions_to_remove_temporarily.extend(["text_reply", "emoji_reply"])
|
||||
elif lian_xu_wen_ben_hui_fu == 2:
|
||||
if probability_roll < 0.8:
|
||||
logger.info(f"{self.log_prefix}[Planner] 连续回复 2 次，80% 概率移除 text_reply 和 emoji_reply (触发)")
|
||||
actions_to_remove_temporarily.extend(["text_reply", "emoji_reply"])
|
||||
else:
|
||||
logger.info(
|
||||
f"{self.log_prefix}[Planner] 连续回复 2 次，80% 概率移除 text_reply 和 emoji_reply (未触发)"
|
||||
)
|
||||
elif lian_xu_wen_ben_hui_fu == 1:
|
||||
if probability_roll < 0.4:
|
||||
logger.info(f"{self.log_prefix}[Planner] 连续回复 1 次，40% 概率移除 text_reply (触发)")
|
||||
actions_to_remove_temporarily.append("text_reply")
|
||||
else:
|
||||
logger.info(f"{self.log_prefix}[Planner] 连续回复 1 次，40% 概率移除 text_reply (未触发)")
|
||||
# --- 结束检查历史动作 ---
|
||||
|
||||
# 获取观察信息
|
||||
observation = self.observations[0]
|
||||
# if is_re_planned: # 暂时简化，不处理重新规划
|
||||
# await observation.observe()
|
||||
observed_messages = observation.talking_message
|
||||
observed_messages_str = observation.talking_message_str_truncate
|
||||
|
||||
# --- 使用 LLM 进行决策 (JSON 输出模式) --- #
|
||||
action = "no_reply" # 默认动作
|
||||
reasoning = "规划器初始化默认"
|
||||
emoji_query = ""
|
||||
llm_error = False # LLM 请求或解析错误标志
|
||||
|
||||
# 获取我们将传递给 prompt 构建器和用于验证的当前可用动作
|
||||
current_available_actions = self.action_manager.get_available_actions()
|
||||
|
||||
try:
|
||||
# --- 应用临时动作移除 ---
|
||||
if actions_to_remove_temporarily:
|
||||
self.action_manager.temporarily_remove_actions(actions_to_remove_temporarily)
|
||||
# 更新 current_available_actions 以反映移除后的状态
|
||||
current_available_actions = self.action_manager.get_available_actions()
|
||||
logger.debug(
|
||||
f"{self.log_prefix}[Planner] 临时移除的动作: {actions_to_remove_temporarily}, 当前可用: {list(current_available_actions.keys())}"
|
||||
)
|
||||
|
||||
# --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
|
||||
prompt = await prompt_builder.build_planner_prompt(
|
||||
is_group_chat=self.is_group_chat, # <-- Pass HFC state
|
||||
chat_target_info=self.chat_target_info, # <-- Pass HFC state
|
||||
cycle_history=self._cycle_history, # <-- Pass HFC state
|
||||
observed_messages_str=observed_messages_str, # <-- Pass local variable
|
||||
current_mind=current_mind, # <-- Pass argument
|
||||
structured_info=self.sub_mind.structured_info_str, # <-- Pass SubMind info
|
||||
current_available_actions=current_available_actions, # <-- Pass determined actions
|
||||
)
|
||||
|
||||
# --- 调用 LLM (普通文本生成) ---
|
||||
llm_content = None
|
||||
try:
|
||||
# 假设 LLMRequest 有 generate_response 方法返回 (content, reasoning, model_name)
|
||||
# 我们只需要 content
|
||||
# !! 注意：这里假设 self.planner_llm 有 generate_response 方法
|
||||
# !! 如果你的 LLMRequest 类使用的是其他方法名，请相应修改
|
||||
llm_content, _, _ = await self.planner_llm.generate_response(prompt=prompt)
|
||||
logger.debug(f"{self.log_prefix}[Planner] LLM 原始 JSON 响应 (预期): {llm_content}")
|
||||
except Exception as req_e:
|
||||
logger.error(f"{self.log_prefix}[Planner] LLM 请求执行失败: {req_e}")
|
||||
reasoning = f"LLM 请求失败: {req_e}"
|
||||
llm_error = True
|
||||
# 直接使用默认动作返回错误结果
|
||||
action = "no_reply" # 明确设置为默认值
|
||||
emoji_query = "" # 明确设置为空
|
||||
# 不再立即返回，而是继续执行 finally 块以恢复动作
|
||||
# return { ... }
|
||||
|
||||
# --- 解析 LLM 返回的 JSON (仅当 LLM 请求未出错时进行) ---
|
||||
if not llm_error and llm_content:
|
||||
try:
|
||||
# 尝试去除可能的 markdown 代码块标记
|
||||
cleaned_content = (
|
||||
llm_content.strip().removeprefix("```json").removeprefix("```").removesuffix("```").strip()
|
||||
)
|
||||
if not cleaned_content:
|
||||
raise json.JSONDecodeError("Cleaned content is empty", cleaned_content, 0)
|
||||
parsed_json = json.loads(cleaned_content)
|
||||
|
||||
# 提取决策，提供默认值
|
||||
extracted_action = parsed_json.get("action", "no_reply")
|
||||
extracted_reasoning = parsed_json.get("reasoning", "LLM未提供理由")
|
||||
extracted_emoji_query = parsed_json.get("emoji_query", "")
|
||||
|
||||
# 验证动作是否在当前可用列表中
|
||||
# !! 使用调用 prompt 时实际可用的动作列表进行验证
|
||||
if extracted_action not in current_available_actions:
|
||||
logger.warning(
|
||||
f"{self.log_prefix}[Planner] LLM 返回了当前不可用或无效的动作: '{extracted_action}' (可用: {list(current_available_actions.keys())})，将强制使用 'no_reply'"
|
||||
)
|
||||
action = "no_reply"
|
||||
reasoning = f"LLM 返回了当前不可用的动作 '{extracted_action}' (可用: {list(current_available_actions.keys())})。原始理由: {extracted_reasoning}"
|
||||
emoji_query = ""
|
||||
# 检查 no_reply 是否也恰好被移除了 (极端情况)
|
||||
if "no_reply" not in current_available_actions:
|
||||
logger.error(
|
||||
f"{self.log_prefix}[Planner] 严重错误：'no_reply' 动作也不可用！无法执行任何动作。"
|
||||
)
|
||||
action = "error" # 回退到错误状态
|
||||
reasoning = "无法执行任何有效动作，包括 no_reply"
|
||||
llm_error = True # 标记为严重错误
|
||||
else:
|
||||
llm_error = False # 视为逻辑修正而非 LLM 错误
|
||||
else:
|
||||
# 动作有效且可用
|
||||
action = extracted_action
|
||||
reasoning = extracted_reasoning
|
||||
emoji_query = extracted_emoji_query
|
||||
llm_error = False # 解析成功
|
||||
logger.debug(
|
||||
f"{self.log_prefix}[要做什么]\nPrompt:\n{prompt}\n\n决策结果 (来自JSON): {action}, 理由: {reasoning}, 表情查询: '{emoji_query}'"
|
||||
)
|
||||
|
||||
except json.JSONDecodeError as json_e:
|
||||
logger.warning(
|
||||
f"{self.log_prefix}[Planner] 解析LLM响应JSON失败: {json_e}. LLM原始输出: '{llm_content}'"
|
||||
)
|
||||
reasoning = f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_reply'."
|
||||
action = "no_reply" # 解析失败则默认不回复
|
||||
emoji_query = ""
|
||||
llm_error = True # 标记解析错误
|
||||
except Exception as parse_e:
|
||||
logger.error(f"{self.log_prefix}[Planner] 处理LLM响应时发生意外错误: {parse_e}")
|
||||
reasoning = f"处理LLM响应时发生意外错误: {parse_e}. 将使用默认动作 'no_reply'."
|
||||
action = "no_reply"
|
||||
emoji_query = ""
|
||||
llm_error = True
|
||||
elif not llm_error and not llm_content:
|
||||
# LLM 请求成功但返回空内容
|
||||
logger.warning(f"{self.log_prefix}[Planner] LLM 返回了空内容。")
|
||||
reasoning = "LLM 返回了空内容，使用默认动作 'no_reply'."
|
||||
action = "no_reply"
|
||||
emoji_query = ""
|
||||
llm_error = True # 标记为空响应错误
|
||||
|
||||
# 如果 llm_error 在此阶段为 True，意味着请求成功但解析失败或返回空
|
||||
# 如果 llm_error 在请求阶段就为 True，则跳过了此解析块
|
||||
|
||||
except Exception as outer_e:
|
||||
logger.error(f"{self.log_prefix}[Planner] Planner 处理过程中发生意外错误: {outer_e}")
|
||||
logger.error(traceback.format_exc())
|
||||
action = "error" # 发生未知错误，标记为 error 动作
|
||||
reasoning = f"Planner 内部处理错误: {outer_e}"
|
||||
emoji_query = ""
|
||||
llm_error = True
|
||||
finally:
|
||||
# --- 确保动作恢复 ---
|
||||
# 检查 self._original_actions_backup 是否有值来判断是否需要恢复
|
||||
if self.action_manager._original_actions_backup is not None:
|
||||
self.action_manager.restore_actions()
|
||||
logger.debug(
|
||||
f"{self.log_prefix}[Planner] 恢复了原始动作集, 当前可用: {list(self.action_manager.get_available_actions().keys())}"
|
||||
)
|
||||
# --- 结束确保动作恢复 ---
|
||||
|
||||
# --- 概率性忽略文本回复附带的表情 (逻辑保持不变) ---
|
||||
if action == "text_reply" and emoji_query:
|
||||
logger.debug(f"{self.log_prefix}[Planner] 大模型建议文字回复带表情: '{emoji_query}'")
|
||||
if random.random() > EMOJI_SEND_PRO:
|
||||
logger.info(
|
||||
f"{self.log_prefix}但是麦麦这次不想加表情 ({1 - EMOJI_SEND_PRO:.0%})，忽略表情 '{emoji_query}'"
|
||||
)
|
||||
emoji_query = "" # 清空表情请求
|
||||
else:
|
||||
logger.info(f"{self.log_prefix}好吧，加上表情 '{emoji_query}'")
|
||||
# --- 结束概率性忽略 ---
|
||||
|
||||
# 返回结果字典
|
||||
return {
|
||||
"action": action,
|
||||
"reasoning": reasoning,
|
||||
"emoji_query": emoji_query,
|
||||
"current_mind": current_mind,
|
||||
"observed_messages": observed_messages,
|
||||
"llm_error": llm_error, # 返回错误状态
|
||||
}
|
||||
|
||||
@@ -17,6 +17,7 @@ from ..knowledge.knowledge_lib import qa_manager
|
||||
import traceback
|
||||
from .heartFC_Cycleinfo import CycleInfo
|
||||
|
||||
|
||||
logger = get_logger("prompt")
|
||||
|
||||
|
||||
@@ -852,6 +853,124 @@ class PromptBuilder:
|
||||
logger.error(traceback.format_exc())
|
||||
return "[构建 Planner Prompt 时出错]"
|
||||
|
||||
async def build_planner_prompt_parallel(
|
||||
self,
|
||||
is_group_chat: bool,
|
||||
chat_target_info: Optional[dict],
|
||||
cycle_history: Deque["CycleInfo"],
|
||||
observed_messages_str: str,
|
||||
structured_info: str,
|
||||
current_available_actions: Dict[str, str],
|
||||
) -> str:
|
||||
"""
|
||||
构建并行规划器的提示词 (不依赖SubMind的思考结果)
|
||||
这个方法与build_planner_prompt类似，但不需要current_mind参数，
|
||||
允许与submind的思考过程并行执行
|
||||
|
||||
参数:
|
||||
is_group_chat: 是否为群聊
|
||||
chat_target_info: 目标聊天信息
|
||||
cycle_history: 循环历史
|
||||
observed_messages_str: 观察到的消息
|
||||
structured_info: 结构化信息字符串
|
||||
current_available_actions: 当前可用的动作
|
||||
|
||||
返回:
|
||||
str: 规划器提示词
|
||||
"""
|
||||
try:
|
||||
# --- Determine chat context ---
|
||||
chat_context_description = "你现在正在一个群聊中"
|
||||
chat_target_name = None # Only relevant for private
|
||||
if not is_group_chat and chat_target_info:
|
||||
chat_target_name = (
|
||||
chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or "对方"
|
||||
)
|
||||
chat_context_description = f"你正在和 {chat_target_name} 私聊"
|
||||
# --- End determining chat context ---
|
||||
|
||||
# Structured info block
|
||||
structured_info_block = ""
|
||||
if structured_info:
|
||||
structured_info_block = f"以下是一些额外的信息：\n{structured_info}\n"
|
||||
|
||||
# Chat content block
|
||||
chat_content_block = ""
|
||||
if observed_messages_str:
|
||||
# Use triple quotes for multi-line string literal
|
||||
chat_content_block = f"""观察到的最新聊天内容如下：
|
||||
---
|
||||
{observed_messages_str}
|
||||
---"""
|
||||
else:
|
||||
chat_content_block = "当前没有观察到新的聊天内容。\\n"
|
||||
|
||||
# Current mind block (并行模式专用)
|
||||
current_mind_block = ""
|
||||
|
||||
# Cycle info block (using passed cycle_history)
|
||||
cycle_info_block = ""
|
||||
recent_active_cycles = []
|
||||
for cycle in reversed(cycle_history):
|
||||
if cycle.action_taken:
|
||||
recent_active_cycles.append(cycle)
|
||||
if len(recent_active_cycles) == 3:
|
||||
break
|
||||
|
||||
consecutive_text_replies = 0
|
||||
responses_for_prompt = []
|
||||
|
||||
for cycle in recent_active_cycles:
|
||||
if cycle.action_type == "text_reply":
|
||||
consecutive_text_replies += 1
|
||||
response_text = cycle.response_info.get("response_text", [])
|
||||
formatted_response = "[空回复]" if not response_text else " ".join(response_text)
|
||||
responses_for_prompt.append(formatted_response)
|
||||
else:
|
||||
break
|
||||
|
||||
if consecutive_text_replies >= 3:
|
||||
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'
|
||||
elif consecutive_text_replies == 2:
|
||||
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'
|
||||
elif consecutive_text_replies == 1:
|
||||
cycle_info_block = f'你刚刚已经回复一条消息（内容: "{responses_for_prompt[0]}"）'
|
||||
|
||||
if cycle_info_block:
|
||||
cycle_info_block = f"\n【近期回复历史】\n{cycle_info_block}\n"
|
||||
else:
|
||||
cycle_info_block = "\n【近期回复历史】\n(最近没有连续文本回复)\n"
|
||||
|
||||
individuality = Individuality.get_instance()
|
||||
prompt_personality = individuality.get_prompt(x_person=2, level=2)
|
||||
|
||||
action_options_text = "当前你可以选择的行动有：\n"
|
||||
action_keys = list(current_available_actions.keys())
|
||||
for name in action_keys:
|
||||
desc = current_available_actions[name]
|
||||
action_options_text += f"- '{name}': {desc}\n"
|
||||
example_action_key = action_keys[0] if action_keys else "no_reply"
|
||||
|
||||
planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
|
||||
|
||||
prompt = planner_prompt_template.format(
|
||||
bot_name=global_config.BOT_NICKNAME,
|
||||
prompt_personality=prompt_personality,
|
||||
chat_context_description=chat_context_description,
|
||||
structured_info_block=structured_info_block,
|
||||
chat_content_block=chat_content_block,
|
||||
current_mind_block=current_mind_block,
|
||||
cycle_info_block=cycle_info_block,
|
||||
action_options_text=action_options_text,
|
||||
example_action=example_action_key,
|
||||
)
|
||||
return prompt
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[PromptBuilder] 构建并行 Planner 提示词时出错: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
return "[构建并行 Planner Prompt 时出错]"
|
||||
|
||||
|
||||
init_prompt()
|
||||
prompt_builder = PromptBuilder()
|
||||
|
||||
Reference in New Issue
Block a user