Merge branch 'dev' into dev
This commit is contained in:
SmartMita
GitHub
@@ -1,8 +1,7 @@
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import asyncio
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import time
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import traceback
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from typing import List, Optional, Dict, Any, TYPE_CHECKING
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import json
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from typing import List, Optional, Dict, Any
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from src.plugins.chat.message import MessageRecv, BaseMessageInfo, MessageThinking, MessageSending
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from src.plugins.chat.message import MessageSet, Seg # Local import needed after move
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from src.plugins.chat.chat_stream import ChatStream
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@@ -17,6 +16,10 @@ from src.plugins.heartFC_chat.heartFC_generator import HeartFCGenerator
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from src.do_tool.tool_use import ToolUser
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from ..chat.message_sender import message_manager # <-- Import the global manager
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from src.plugins.chat.emoji_manager import emoji_manager
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from src.plugins.utils.json_utils import process_llm_tool_response # 导入新的JSON工具
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from src.heart_flow.sub_mind import SubMind
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from src.heart_flow.observation import Observation
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from src.plugins.heartFC_chat.heartflow_prompt_builder import global_prompt_manager
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# --- End import ---
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@@ -31,13 +34,6 @@ interest_log_config = LogConfig(
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logger = get_module_logger("HeartFCLoop", config=interest_log_config) # Logger Name Changed
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# Forward declaration for type hinting
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if TYPE_CHECKING:
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# Keep this if HeartFCController methods are still needed elsewhere,
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# but the instance variable will be removed from HeartFChatting
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# from .heartFC_controler import HeartFCController
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from src.heart_flow.heartflow import SubHeartflow, heartflow # <-- 同时导入 heartflow 实例用于类型检查
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PLANNER_TOOL_DEFINITION = [
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{
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"type": "function",
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@@ -72,7 +68,7 @@ class HeartFChatting:
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其生命周期现在由其关联的 SubHeartflow 的 FOCUSED 状态控制。
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"""
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def __init__(self, chat_id: str):
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def __init__(self, chat_id: str, sub_mind: SubMind, observations: Observation):
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"""
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HeartFChatting 初始化函数
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@@ -82,7 +78,8 @@ class HeartFChatting:
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# 基础属性
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self.stream_id: str = chat_id # 聊天流ID
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self.chat_stream: Optional[ChatStream] = None # 关联的聊天流
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self.sub_hf: SubHeartflow = None # 关联的子心流
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self.sub_mind: SubMind = sub_mind # 关联的子思维
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self.observations: Observation = observations # 关联的观察
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# 初始化状态控制
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self._initialized = False # 是否已初始化标志
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@@ -119,18 +116,10 @@ class HeartFChatting:
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log_prefix = self._get_log_prefix() # 获取前缀
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try:
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self.chat_stream = chat_manager.get_stream(self.stream_id)
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if not self.chat_stream:
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logger.error(f"{log_prefix} 获取ChatStream失败。")
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return False
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# <-- 在这里导入 heartflow 实例
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from src.heart_flow.heartflow import heartflow
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self.sub_hf = heartflow.get_subheartflow(self.stream_id)
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if not self.sub_hf:
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logger.warning(f"{log_prefix} 获取SubHeartflow失败。一些功能可能受限。")
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self._initialized = True
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logger.info(f"麦麦感觉到了，激发了HeartFChatting{log_prefix} 初始化成功。")
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return True
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@@ -245,9 +234,6 @@ class HeartFChatting:
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action = planner_result.get("action", "error")
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reasoning = planner_result.get("reasoning", "Planner did not provide reasoning.")
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emoji_query = planner_result.get("emoji_query", "")
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# current_mind = planner_result.get("current_mind", "[Mind unavailable]")
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# send_emoji_from_tools = planner_result.get("send_emoji_from_tools", "") # Emoji from tools
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observed_messages = planner_result.get("observed_messages", [])
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llm_error = planner_result.get("llm_error", False)
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if llm_error:
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@@ -259,7 +245,7 @@ class HeartFChatting:
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elif action == "text_reply":
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logger.debug(f"{log_prefix} HeartFChatting: 麦麦决定回复文本. 理由: {reasoning}")
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action_taken_this_cycle = True
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anchor_message = await self._get_anchor_message(observed_messages)
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anchor_message = await self._get_anchor_message()
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if not anchor_message:
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logger.error(f"{log_prefix} 循环: 无法获取锚点消息用于回复. 跳过周期.")
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else:
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@@ -304,7 +290,7 @@ class HeartFChatting:
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f"{log_prefix} HeartFChatting: 麦麦决定回复表情 ('{emoji_query}'). 理由: {reasoning}"
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)
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action_taken_this_cycle = True
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anchor = await self._get_anchor_message(observed_messages)
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anchor = await self._get_anchor_message()
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if anchor:
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try:
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# --- Handle Emoji (Moved) --- #
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@@ -322,19 +308,13 @@ class HeartFChatting:
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# --- 新增：等待新消息 ---
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logger.debug(f"{log_prefix} HeartFChatting: 开始等待新消息 (自 {planner_start_db_time})...")
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observation = None
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if self.sub_hf:
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observation = self.sub_hf._get_primary_observation()
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observation = self.observations[0]
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if observation:
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with Timer("Wait New Msg", cycle_timers): # <--- Start Wait timer
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wait_start_time = time.monotonic()
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while True:
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# Removed timer check within wait loop
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# async with self._timer_lock:
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# if self._loop_timer <= 0:
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# logger.info(f"{log_prefix} HeartFChatting: 等待新消息时计时器耗尽。")
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# break # 计时器耗尽，退出等待
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# 检查是否有新消息
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has_new = await observation.has_new_messages_since(planner_start_db_time)
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if has_new:
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@@ -395,14 +375,6 @@ class HeartFChatting:
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self._processing_lock.release()
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# logger.trace(f"{log_prefix} 循环释放了处理锁.") # Reduce noise
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# --- Timer Decrement Logging Removed ---
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# async with self._timer_lock:
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# self._loop_timer -= cycle_duration
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# # Log timer decrement less aggressively
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# if cycle_duration > 0.1 or not action_taken_this_cycle:
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# logger.debug(
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# f"{log_prefix} HeartFChatting: 周期耗时 {cycle_duration:.2f}s. 剩余时间: {self._loop_timer:.1f}s."
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# )
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if cycle_duration > 0.1:
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logger.debug(f"{log_prefix} HeartFChatting: 周期耗时 {cycle_duration:.2f}s.")
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@@ -437,77 +409,34 @@ class HeartFChatting:
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"""
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log_prefix = self._get_log_prefix()
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observed_messages: List[dict] = []
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tool_result_info = {}
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get_mid_memory_id = []
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# send_emoji_from_tools = "" # Emoji suggested by tools
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current_mind: Optional[str] = None
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llm_error = False # Flag for LLM failure
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# --- Ensure SubHeartflow is available ---
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if not self.sub_hf:
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# Attempt to re-fetch if missing (might happen if initialization order changes)
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self.sub_hf = heartflow.get_subheartflow(self.stream_id)
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if not self.sub_hf:
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logger.error(f"{log_prefix}[Planner] SubHeartflow is not available. Cannot proceed.")
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return {
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"action": "error",
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"reasoning": "SubHeartflow unavailable",
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"llm_error": True,
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"observed_messages": [],
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}
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current_mind: Optional[str] = None
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llm_error = False
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try:
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# Access observation via self.sub_hf
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observation = self.sub_hf._get_primary_observation()
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observation = self.observations[0]
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await observation.observe()
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observed_messages = observation.talking_message
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observed_messages_str = observation.talking_message_str
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except Exception as e:
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logger.error(f"{log_prefix}[Planner] 获取观察信息时出错: {e}")
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# Handle error gracefully, maybe return an error state
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observed_messages_str = "[Error getting observation]"
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# Consider returning error here if observation is critical
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# --- 结束获取观察信息 --- #
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# --- (Moved from _replier_work) 1. 思考前使用工具 --- #
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try:
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# Access tool_user directly
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tool_result = await self.tool_user.use_tool(
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message_txt=observed_messages_str,
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chat_stream=self.chat_stream,
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observation=self.sub_hf._get_primary_observation(),
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)
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if tool_result.get("used_tools", False):
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tool_result_info = tool_result.get("structured_info", {})
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logger.debug(f"{log_prefix}[Planner] 规划前工具结果: {tool_result_info}")
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get_mid_memory_id = [
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mem["content"] for mem in tool_result_info.get("mid_chat_mem", []) if "content" in mem
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]
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except Exception as e_tool:
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logger.error(f"{log_prefix}[Planner] 规划前工具使用失败: {e_tool}")
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# --- 结束工具使用 --- #
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# --- (Moved from _replier_work) 2. SubHeartflow 思考 --- #
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try:
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current_mind, _past_mind = await self.sub_hf.do_thinking_before_reply(
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extra_info=tool_result_info,
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obs_id=get_mid_memory_id,
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)
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# logger.debug(f"{log_prefix}[Planner] SubHF Mind: {current_mind}")
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current_mind, _past_mind = await self.sub_mind.do_thinking_before_reply()
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except Exception as e_subhf:
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logger.error(f"{log_prefix}[Planner] SubHeartflow 思考失败: {e_subhf}")
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current_mind = "[思考时出错]"
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# --- 结束 SubHeartflow 思考 --- #
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# --- 使用 LLM 进行决策 --- #
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action = "no_reply" # Default action
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emoji_query = "" # Default emoji query (used if action is emoji_reply or text_reply with emoji)
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action = "no_reply" # 默认动作
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emoji_query = "" # 默认表情查询
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reasoning = "默认决策或获取决策失败"
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llm_error = False # LLM错误标志
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try:
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prompt = await self._build_planner_prompt(observed_messages_str, current_mind)
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prompt = await self._build_planner_prompt(
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observed_messages_str, current_mind, self.sub_mind.structured_info
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)
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payload = {
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"model": self.planner_llm.model_name,
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"messages": [{"role": "user", "content": prompt}],
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@@ -515,83 +444,66 @@ class HeartFChatting:
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"tool_choice": {"type": "function", "function": {"name": "decide_reply_action"}},
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}
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response = await self.planner_llm._execute_request(
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endpoint="/chat/completions", payload=payload, prompt=prompt
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# 执行LLM请求
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try:
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response = await self.planner_llm._execute_request(
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endpoint="/chat/completions", payload=payload, prompt=prompt
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)
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except Exception as req_e:
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logger.error(f"{log_prefix}[Planner] LLM请求执行失败: {req_e}")
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return {
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"action": "error",
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"reasoning": f"LLM请求执行失败: {req_e}",
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"emoji_query": "",
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"current_mind": current_mind,
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"observed_messages": observed_messages,
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"llm_error": True,
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}
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# 使用辅助函数处理工具调用响应
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success, arguments, error_msg = process_llm_tool_response(
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response, expected_tool_name="decide_reply_action", log_prefix=f"{log_prefix}[Planner] "
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)
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if len(response) == 3:
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_, _, tool_calls = response
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if tool_calls and isinstance(tool_calls, list) and len(tool_calls) > 0:
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tool_call = tool_calls[0]
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if (
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tool_call.get("type") == "function"
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and tool_call.get("function", {}).get("name") == "decide_reply_action"
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):
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try:
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arguments = json.loads(tool_call["function"]["arguments"])
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action = arguments.get("action", "no_reply")
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reasoning = arguments.get("reasoning", "未提供理由")
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# Planner explicitly provides emoji query if action is emoji_reply or text_reply wants emoji
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emoji_query = arguments.get("emoji_query", "")
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logger.debug(
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f"{log_prefix}[Planner] LLM Prompt: {prompt}\n决策: {action}, 理由: {reasoning}, EmojiQuery: '{emoji_query}'"
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)
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except json.JSONDecodeError as json_e:
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logger.error(
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f"{log_prefix}[Planner] 解析工具参数失败: {json_e}. Args: {tool_call['function'].get('arguments')}"
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)
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action = "error"
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||||
reasoning = "工具参数解析失败"
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||||
llm_error = True
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||||
except Exception as parse_e:
|
||||
logger.error(f"{log_prefix}[Planner] 处理工具参数时出错: {parse_e}")
|
||||
action = "error"
|
||||
reasoning = "处理工具参数时出错"
|
||||
llm_error = True
|
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else:
|
||||
logger.warning(
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f"{log_prefix}[Planner] LLM 未按预期调用 'decide_reply_action' 工具。Tool calls: {tool_calls}"
|
||||
)
|
||||
action = "error"
|
||||
reasoning = "LLM未调用预期工具"
|
||||
llm_error = True
|
||||
else:
|
||||
logger.warning(f"{log_prefix}[Planner] LLM 响应中未包含有效的工具调用。Tool calls: {tool_calls}")
|
||||
action = "error"
|
||||
reasoning = "LLM响应无工具调用"
|
||||
llm_error = True
|
||||
if success:
|
||||
# 提取决策参数
|
||||
action = arguments.get("action", "no_reply")
|
||||
reasoning = arguments.get("reasoning", "未提供理由")
|
||||
emoji_query = arguments.get("emoji_query", "")
|
||||
|
||||
# 记录决策结果
|
||||
logger.debug(f"{log_prefix}[Planner] 决策结果: {action}, 理由: {reasoning}, 表情查询: '{emoji_query}'")
|
||||
else:
|
||||
logger.warning(f"{log_prefix}[Planner] LLM 未返回预期的工具调用响应。Response parts: {len(response)}")
|
||||
# 处理工具调用失败
|
||||
logger.warning(f"{log_prefix}[Planner] {error_msg}")
|
||||
action = "error"
|
||||
reasoning = "LLM响应格式错误"
|
||||
reasoning = error_msg
|
||||
llm_error = True
|
||||
|
||||
except Exception as llm_e:
|
||||
logger.error(f"{log_prefix}[Planner] Planner LLM 调用失败: {llm_e}")
|
||||
# logger.error(traceback.format_exc()) # Maybe too verbose for loop?
|
||||
logger.error(f"{log_prefix}[Planner] Planner LLM处理过程中出错: {llm_e}")
|
||||
logger.error(traceback.format_exc()) # 记录完整堆栈以便调试
|
||||
action = "error"
|
||||
reasoning = f"LLM 调用失败: {llm_e}"
|
||||
reasoning = f"LLM处理失败: {llm_e}"
|
||||
llm_error = True
|
||||
# --- 结束 LLM 决策 --- #
|
||||
|
||||
return {
|
||||
"action": action,
|
||||
"reasoning": reasoning,
|
||||
"emoji_query": emoji_query, # Explicit query from Planner/LLM
|
||||
"emoji_query": emoji_query,
|
||||
"current_mind": current_mind,
|
||||
# "send_emoji_from_tools": send_emoji_from_tools, # Emoji suggested by tools (used as fallback)
|
||||
"observed_messages": observed_messages,
|
||||
"llm_error": llm_error,
|
||||
}
|
||||
|
||||
async def _get_anchor_message(self, observed_messages: List[dict]) -> Optional[MessageRecv]:
|
||||
async def _get_anchor_message(self) -> Optional[MessageRecv]:
|
||||
"""
|
||||
重构观察到的最后一条消息作为回复的锚点，
|
||||
如果重构失败或观察为空，则创建一个占位符。
|
||||
"""
|
||||
|
||||
try:
|
||||
# --- Create Placeholder --- #
|
||||
placeholder_id = f"mid_pf_{int(time.time() * 1000)}"
|
||||
placeholder_user = UserInfo(
|
||||
user_id="system_trigger", user_nickname="System Trigger", platform=self.chat_stream.platform
|
||||
@@ -652,64 +564,67 @@ class HeartFChatting:
|
||||
raise RuntimeError("发送回复失败，_send_response_messages返回None")
|
||||
|
||||
async def shutdown(self):
|
||||
"""
|
||||
Gracefully shuts down the HeartFChatting instance by cancelling the active loop task.
|
||||
"""
|
||||
"""优雅关闭HeartFChatting实例，取消活动循环任务"""
|
||||
log_prefix = self._get_log_prefix()
|
||||
logger.info(f"{log_prefix} Shutting down HeartFChatting...")
|
||||
logger.info(f"{log_prefix} 正在关闭HeartFChatting...")
|
||||
|
||||
# 取消循环任务
|
||||
if self._loop_task and not self._loop_task.done():
|
||||
logger.info(f"{log_prefix} Cancelling active PF loop task.")
|
||||
logger.info(f"{log_prefix} 正在取消HeartFChatting循环任务")
|
||||
self._loop_task.cancel()
|
||||
try:
|
||||
await asyncio.wait_for(self._loop_task, timeout=1.0) # Shorter timeout?
|
||||
except asyncio.CancelledError:
|
||||
logger.info(f"{log_prefix} PF loop task cancelled successfully.")
|
||||
except asyncio.TimeoutError:
|
||||
logger.warning(f"{log_prefix} Timeout waiting for PF loop task cancellation.")
|
||||
await asyncio.wait_for(self._loop_task, timeout=1.0)
|
||||
logger.info(f"{log_prefix} HeartFChatting循环任务已取消")
|
||||
except (asyncio.CancelledError, asyncio.TimeoutError):
|
||||
pass
|
||||
except Exception as e:
|
||||
logger.error(f"{log_prefix} Error during loop task cancellation: {e}")
|
||||
logger.error(f"{log_prefix} 取消循环任务出错: {e}")
|
||||
else:
|
||||
logger.info(f"{log_prefix} No active PF loop task found to cancel.")
|
||||
logger.info(f"{log_prefix} 没有活动的HeartFChatting循环任务")
|
||||
|
||||
# 清理状态
|
||||
self._loop_active = False
|
||||
self._loop_task = None
|
||||
if self._processing_lock.locked():
|
||||
logger.warning(f"{log_prefix} Releasing processing lock during shutdown.")
|
||||
self._processing_lock.release()
|
||||
logger.info(f"{log_prefix} HeartFChatting shutdown complete.")
|
||||
logger.warning(f"{log_prefix} 已释放处理锁")
|
||||
|
||||
async def _build_planner_prompt(self, observed_messages_str: str, current_mind: Optional[str]) -> str:
|
||||
logger.info(f"{log_prefix} HeartFChatting关闭完成")
|
||||
|
||||
async def _build_planner_prompt(
|
||||
self, observed_messages_str: str, current_mind: Optional[str], structured_info: Dict[str, Any]
|
||||
) -> str:
|
||||
"""构建 Planner LLM 的提示词"""
|
||||
|
||||
prompt = f"你的名字是 {global_config.BOT_NICKNAME}。是一个女大二学生，正在QQ聊天，正在决定是否以及如何回应当前的聊天。\n"
|
||||
|
||||
|
||||
# 准备结构化信息块
|
||||
structured_info_block = ""
|
||||
if structured_info:
|
||||
structured_info_block = f"以下是一些额外的信息：\n{structured_info}\n"
|
||||
|
||||
# 准备聊天内容块
|
||||
chat_content_block = ""
|
||||
if observed_messages_str:
|
||||
prompt += "观察到的最新聊天内容如下 (最近的消息在最后)：\n---\n"
|
||||
prompt += observed_messages_str
|
||||
prompt += "\n---"
|
||||
chat_content_block = "观察到的最新聊天内容如下 (最近的消息在最后)：\n---\n"
|
||||
chat_content_block += observed_messages_str
|
||||
chat_content_block += "\n---"
|
||||
else:
|
||||
prompt += "当前没有观察到新的聊天内容。\n"
|
||||
|
||||
prompt += "\n看了以上内容，你产生的内心想法是："
|
||||
chat_content_block = "当前没有观察到新的聊天内容。\n"
|
||||
|
||||
# 准备当前思维块
|
||||
current_mind_block = ""
|
||||
if current_mind:
|
||||
prompt += f"\n---\n{current_mind}\n---\n\n"
|
||||
current_mind_block = f"\n---\n{current_mind}\n---\n\n"
|
||||
else:
|
||||
prompt += " [没有特别的想法] \n\n"
|
||||
|
||||
prompt += (
|
||||
"请结合你的内心想法和观察到的聊天内容，分析情况并使用 'decide_reply_action' 工具来决定你的最终行动。\n"
|
||||
"决策依据：\n"
|
||||
"1. 如果聊天内容无聊、与你无关、或者你的内心想法认为不适合回复（例如在讨论你不懂或不感兴趣的话题），选择 'no_reply'。\n"
|
||||
"2. 如果聊天内容值得回应，且适合用文字表达（参考你的内心想法），选择 'text_reply'。如果你有情绪想表达，想在文字后追加一个表达情绪的表情，请同时提供 'emoji_query' (例如：'开心的'、'惊讶的')。\n"
|
||||
"3. 如果聊天内容或你的内心想法适合用一个表情来回应（例如表示赞同、惊讶、无语等），选择 'emoji_reply' 并提供表情主题 'emoji_query'。\n"
|
||||
"4. 如果最后一条消息是你自己发的，并且之后没有人回复你，通常选择 'no_reply'，除非有特殊原因需要追问。\n"
|
||||
"5. 除非大家都在这么做，或者有特殊理由，否则不要重复别人刚刚说过的话或简单附和。\n"
|
||||
"6. 表情包是用来表达情绪的，不要直接回复或评价别人的表情包，而是根据对话内容和情绪选择是否用表情回应。\n"
|
||||
"7. 如果观察到的内容只有你自己的发言，选择 'no_reply'。\n"
|
||||
"8. 不要回复你自己的话，不要把自己的话当做别人说的。\n"
|
||||
"必须调用 'decide_reply_action' 工具并提供 'action' 和 'reasoning'。如果选择了 'emoji_reply' 或者选择了 'text_reply' 并想追加表情，则必须提供 'emoji_query'。"
|
||||
current_mind_block = " [没有特别的想法] \n\n"
|
||||
|
||||
# 获取提示词模板并填充数据
|
||||
prompt = (await global_prompt_manager.get_prompt_async("planner_prompt")).format(
|
||||
bot_name=global_config.BOT_NICKNAME,
|
||||
structured_info_block=structured_info_block,
|
||||
chat_content_block=chat_content_block,
|
||||
current_mind_block=current_mind_block,
|
||||
)
|
||||
|
||||
|
||||
return prompt
|
||||
|
||||
# --- 回复器 (Replier) 的定义 --- #
|
||||
@@ -726,7 +641,8 @@ class HeartFChatting:
|
||||
response_set: Optional[List[str]] = None
|
||||
try:
|
||||
response_set = await self.gpt_instance.generate_response(
|
||||
current_mind_info=self.sub_hf.current_mind,
|
||||
structured_info=self.sub_mind.structured_info,
|
||||
current_mind_info=self.sub_mind.current_mind,
|
||||
reason=reason,
|
||||
message=anchor_message, # Pass anchor_message positionally (matches 'message' parameter)
|
||||
thinking_id=thinking_id, # Pass thinking_id positionally
|
||||
@@ -736,8 +652,6 @@ class HeartFChatting:
|
||||
logger.warning(f"{log_prefix}[Replier-{thinking_id}] LLM生成了一个空回复集。")
|
||||
return None
|
||||
|
||||
# --- 准备并返回结果 --- #
|
||||
# logger.info(f"{log_prefix}[Replier-{thinking_id}] 成功生成了回复集: {' '.join(response_set)[:50]}...")
|
||||
return response_set
|
||||
|
||||
except Exception as e:
|
||||
@@ -782,7 +696,6 @@ class HeartFChatting:
|
||||
return None
|
||||
|
||||
chat = anchor_message.chat_stream
|
||||
# Access MessageManager directly
|
||||
container = await message_manager.get_container(chat.stream_id)
|
||||
thinking_message = None
|
||||
|
||||
|
||||
@@ -39,6 +39,7 @@ class HeartFCGenerator:
|
||||
|
||||
async def generate_response(
|
||||
self,
|
||||
structured_info: str,
|
||||
current_mind_info: str,
|
||||
reason: str,
|
||||
message: MessageRecv,
|
||||
@@ -46,17 +47,13 @@ class HeartFCGenerator:
|
||||
) -> Optional[List[str]]:
|
||||
"""根据当前模型类型选择对应的生成函数"""
|
||||
|
||||
logger.info(
|
||||
f"思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
|
||||
)
|
||||
|
||||
arousal_multiplier = MoodManager.get_instance().get_arousal_multiplier()
|
||||
|
||||
with Timer() as t_generate_response:
|
||||
current_model = self.model_normal
|
||||
current_model.temperature = global_config.llm_normal["temp"] * arousal_multiplier # 激活度越高，温度越高
|
||||
model_response = await self._generate_response_with_model(
|
||||
current_mind_info, reason, message, current_model, thinking_id
|
||||
structured_info, current_mind_info, reason, message, current_model, thinking_id
|
||||
)
|
||||
|
||||
if model_response:
|
||||
@@ -71,28 +68,33 @@ class HeartFCGenerator:
|
||||
return None
|
||||
|
||||
async def _generate_response_with_model(
|
||||
self, current_mind_info: str, reason: str, message: MessageRecv, model: LLMRequest, thinking_id: str
|
||||
self,
|
||||
structured_info: str,
|
||||
current_mind_info: str,
|
||||
reason: str,
|
||||
message: MessageRecv,
|
||||
model: LLMRequest,
|
||||
thinking_id: str,
|
||||
) -> str:
|
||||
sender_name = ""
|
||||
|
||||
info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
|
||||
|
||||
sender_name = f"<{message.chat_stream.user_info.platform}:{message.chat_stream.user_info.user_id}:{message.chat_stream.user_info.user_nickname}:{message.chat_stream.user_info.user_cardname}>"
|
||||
|
||||
with Timer() as t_build_prompt:
|
||||
prompt = await prompt_builder.build_prompt(
|
||||
build_mode="focus",
|
||||
reason=reason,
|
||||
current_mind_info=current_mind_info,
|
||||
message_txt=message.processed_plain_text,
|
||||
sender_name=sender_name,
|
||||
structured_info=structured_info,
|
||||
message_txt="",
|
||||
sender_name="",
|
||||
chat_stream=message.chat_stream,
|
||||
)
|
||||
logger.info(f"构建prompt时间: {t_build_prompt.human_readable}")
|
||||
# logger.info(f"构建prompt时间: {t_build_prompt.human_readable}")
|
||||
|
||||
try:
|
||||
content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
|
||||
|
||||
logger.info(f"\nprompt:{prompt}\n生成回复{content}\n")
|
||||
|
||||
info_catcher.catch_after_llm_generated(
|
||||
prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=self.current_model_name
|
||||
)
|
||||
@@ -103,106 +105,6 @@ class HeartFCGenerator:
|
||||
|
||||
return content
|
||||
|
||||
async def _get_emotion_tags(self, content: str, processed_plain_text: str):
|
||||
"""提取情感标签，结合立场和情绪"""
|
||||
try:
|
||||
# 构建提示词，结合回复内容、被回复的内容以及立场分析
|
||||
prompt = f"""
|
||||
请严格根据以下对话内容，完成以下任务：
|
||||
1. 判断回复者对被回复者观点的直接立场：
|
||||
- "支持"：明确同意或强化被回复者观点
|
||||
- "反对"：明确反驳或否定被回复者观点
|
||||
- "中立"：不表达明确立场或无关回应
|
||||
2. 从"开心,愤怒,悲伤,惊讶,平静,害羞,恐惧,厌恶,困惑"中选出最匹配的1个情感标签
|
||||
3. 按照"立场-情绪"的格式直接输出结果，例如："反对-愤怒"
|
||||
4. 考虑回复者的人格设定为{global_config.personality_core}
|
||||
|
||||
对话示例：
|
||||
被回复：「A就是笨」
|
||||
回复：「A明明很聪明」 → 反对-愤怒
|
||||
|
||||
当前对话：
|
||||
被回复：「{processed_plain_text}」
|
||||
回复：「{content}」
|
||||
|
||||
输出要求：
|
||||
- 只需输出"立场-情绪"结果，不要解释
|
||||
- 严格基于文字直接表达的对立关系判断
|
||||
"""
|
||||
|
||||
# 调用模型生成结果
|
||||
result, _, _ = await self.model_sum.generate_response(prompt)
|
||||
result = result.strip()
|
||||
|
||||
# 解析模型输出的结果
|
||||
if "-" in result:
|
||||
stance, emotion = result.split("-", 1)
|
||||
valid_stances = ["支持", "反对", "中立"]
|
||||
valid_emotions = ["开心", "愤怒", "悲伤", "惊讶", "害羞", "平静", "恐惧", "厌恶", "困惑"]
|
||||
if stance in valid_stances and emotion in valid_emotions:
|
||||
return stance, emotion # 返回有效的立场-情绪组合
|
||||
else:
|
||||
logger.debug(f"无效立场-情感组合:{result}")
|
||||
return "中立", "平静" # 默认返回中立-平静
|
||||
else:
|
||||
logger.debug(f"立场-情感格式错误:{result}")
|
||||
return "中立", "平静" # 格式错误时返回默认值
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"获取情感标签时出错: {e}")
|
||||
return "中立", "平静" # 出错时返回默认值
|
||||
|
||||
async def _get_emotion_tags_with_reason(self, content: str, processed_plain_text: str, reason: str):
|
||||
"""提取情感标签，结合立场和情绪"""
|
||||
try:
|
||||
# 构建提示词，结合回复内容、被回复的内容以及立场分析
|
||||
prompt = f"""
|
||||
请严格根据以下对话内容，完成以下任务：
|
||||
1. 判断回复者对被回复者观点的直接立场：
|
||||
- "支持"：明确同意或强化被回复者观点
|
||||
- "反对"：明确反驳或否定被回复者观点
|
||||
- "中立"：不表达明确立场或无关回应
|
||||
2. 从"开心,愤怒,悲伤,惊讶,平静,害羞,恐惧,厌恶,困惑"中选出最匹配的1个情感标签
|
||||
3. 按照"立场-情绪"的格式直接输出结果，例如："反对-愤怒"
|
||||
4. 考虑回复者的人格设定为{global_config.personality_core}
|
||||
|
||||
对话示例：
|
||||
被回复：「A就是笨」
|
||||
回复：「A明明很聪明」 → 反对-愤怒
|
||||
|
||||
当前对话：
|
||||
被回复：「{processed_plain_text}」
|
||||
回复：「{content}」
|
||||
|
||||
原因：「{reason}」
|
||||
|
||||
输出要求：
|
||||
- 只需输出"立场-情绪"结果，不要解释
|
||||
- 严格基于文字直接表达的对立关系判断
|
||||
"""
|
||||
|
||||
# 调用模型生成结果
|
||||
result, _, _ = await self.model_sum.generate_response(prompt)
|
||||
result = result.strip()
|
||||
|
||||
# 解析模型输出的结果
|
||||
if "-" in result:
|
||||
stance, emotion = result.split("-", 1)
|
||||
valid_stances = ["支持", "反对", "中立"]
|
||||
valid_emotions = ["开心", "愤怒", "悲伤", "惊讶", "害羞", "平静", "恐惧", "厌恶", "困惑"]
|
||||
if stance in valid_stances and emotion in valid_emotions:
|
||||
return stance, emotion # 返回有效的立场-情绪组合
|
||||
else:
|
||||
logger.debug(f"无效立场-情感组合:{result}")
|
||||
return "中立", "平静" # 默认返回中立-平静
|
||||
else:
|
||||
logger.debug(f"立场-情感格式错误:{result}")
|
||||
return "中立", "平静" # 格式错误时返回默认值
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"获取情感标签时出错: {e}")
|
||||
return "中立", "平静" # 出错时返回默认值
|
||||
|
||||
async def _process_response(self, content: str) -> List[str]:
|
||||
"""处理响应内容，返回处理后的内容和情感标签"""
|
||||
if not content:
|
||||
|
||||
@@ -21,19 +21,42 @@ logger = get_module_logger("prompt")
|
||||
def init_prompt():
|
||||
Prompt(
|
||||
"""
|
||||
你有以下信息可供参考：
|
||||
{structured_info}
|
||||
{chat_target}
|
||||
{chat_talking_prompt}
|
||||
现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言或者回复这条消息。\n
|
||||
你的网名叫{bot_name}，{prompt_personality} {prompt_identity}。
|
||||
你正在{chat_target_2},现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，
|
||||
现在你想要在群里发言或者回复。\n
|
||||
你需要扮演一位网名叫{bot_name}的人进行回复，这个人的特点是："{prompt_personality} {prompt_identity}"。
|
||||
你正在{chat_target_2},现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，你可以参考贴吧，小红书或者微博的回复风格。
|
||||
你刚刚脑子里在想：
|
||||
{current_mind_info}
|
||||
{reason}
|
||||
回复尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。请一次只回复一个话题，不要同时回复多个人。{prompt_ger}
|
||||
请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话 ，注意只输出回复内容。
|
||||
回复尽量简短一些。请注意把握聊天内容，不要回复的太有条理，可以有个性。请一次只回复一个话题，不要同时回复多个人，不用指出你回复的是谁。{prompt_ger}
|
||||
请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，不要说你说过的话题 ，注意只输出回复内容。
|
||||
{moderation_prompt}。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 )。""",
|
||||
"heart_flow_prompt",
|
||||
)
|
||||
|
||||
# Planner提示词
|
||||
Prompt(
|
||||
"""你的名字是 {bot_name}。是一个女大二学生，正在QQ聊天，正在决定是否以及如何回应当前的聊天。
|
||||
{structured_info_block}
|
||||
{chat_content_block}
|
||||
看了以上内容，你产生的内心想法是：
|
||||
{current_mind_block}
|
||||
请结合你的内心想法和观察到的聊天内容，分析情况并使用 'decide_reply_action' 工具来决定你的最终行动。
|
||||
决策依据：
|
||||
1. 如果聊天内容无聊、与你无关、或者你的内心想法认为不适合回复（例如在讨论你不懂或不感兴趣的话题），选择 'no_reply'。
|
||||
2. 如果聊天内容值得回应，且适合用文字表达（参考你的内心想法），选择 'text_reply'。如果你有情绪想表达，想在文字后追加一个表达情绪的表情，请同时提供 'emoji_query' (例如：'开心的'、'惊讶的')。
|
||||
3. 如果聊天内容或你的内心想法适合用一个表情来回应（例如表示赞同、惊讶、无语等），选择 'emoji_reply' 并提供表情主题 'emoji_query'。
|
||||
4. 如果最后一条消息是你自己发的，观察到的内容只有你自己的发言，并且之后没有人回复你，通常选择 'no_reply'，除非有特殊原因需要追问。
|
||||
5. 如果聊天记录中最新的消息是你自己发送的，并且你还想继续回复，你应该紧紧衔接你发送的消息，进行话题的深入，补充，或追问等等；。
|
||||
6. 表情包是用来表达情绪的，不要直接回复或评价别人的表情包，而是根据对话内容和情绪选择是否用表情回应。
|
||||
7. 不要回复你自己的话，不要把自己的话当做别人说的。
|
||||
必须调用 'decide_reply_action' 工具并提供 'action' 和 'reasoning'。如果选择了 'emoji_reply' 或者选择了 'text_reply' 并想追加表情，则必须提供 'emoji_query'。""",
|
||||
"planner_prompt",
|
||||
)
|
||||
|
||||
Prompt("你正在qq群里聊天，下面是群里在聊的内容：", "chat_target_group1")
|
||||
Prompt("和群里聊天", "chat_target_group2")
|
||||
Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
|
||||
@@ -79,17 +102,26 @@ class PromptBuilder:
|
||||
self.activate_messages = ""
|
||||
|
||||
async def build_prompt(
|
||||
self, build_mode, reason, current_mind_info, message_txt: str, sender_name: str = "某人", chat_stream=None
|
||||
self,
|
||||
build_mode,
|
||||
reason,
|
||||
current_mind_info,
|
||||
structured_info,
|
||||
message_txt: str,
|
||||
sender_name: str = "某人",
|
||||
chat_stream=None,
|
||||
) -> Optional[tuple[str, str]]:
|
||||
if build_mode == "normal":
|
||||
return await self._build_prompt_normal(chat_stream, message_txt, sender_name)
|
||||
|
||||
elif build_mode == "focus":
|
||||
return await self._build_prompt_focus(reason, current_mind_info, chat_stream, message_txt, sender_name)
|
||||
return await self._build_prompt_focus(
|
||||
reason, current_mind_info, structured_info, chat_stream,
|
||||
)
|
||||
return None
|
||||
|
||||
async def _build_prompt_focus(
|
||||
self, reason, current_mind_info, chat_stream, message_txt: str, sender_name: str = "某人"
|
||||
self, reason, current_mind_info, structured_info, chat_stream
|
||||
) -> tuple[str, str]:
|
||||
individuality = Individuality.get_instance()
|
||||
prompt_personality = individuality.get_prompt(type="personality", x_person=2, level=1)
|
||||
@@ -117,26 +149,6 @@ class PromptBuilder:
|
||||
read_mark=0.0,
|
||||
)
|
||||
|
||||
# 关键词检测与反应
|
||||
keywords_reaction_prompt = ""
|
||||
for rule in global_config.keywords_reaction_rules:
|
||||
if rule.get("enable", False):
|
||||
if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
|
||||
logger.info(
|
||||
f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
|
||||
)
|
||||
keywords_reaction_prompt += rule.get("reaction", "") + "，"
|
||||
else:
|
||||
for pattern in rule.get("regex", []):
|
||||
result = pattern.search(message_txt)
|
||||
if result:
|
||||
reaction = rule.get("reaction", "")
|
||||
for name, content in result.groupdict().items():
|
||||
reaction = reaction.replace(f"[{name}]", content)
|
||||
logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
|
||||
keywords_reaction_prompt += reaction + "，"
|
||||
break
|
||||
|
||||
# 中文高手(新加的好玩功能)
|
||||
prompt_ger = ""
|
||||
if random.random() < 0.04:
|
||||
@@ -148,12 +160,11 @@ class PromptBuilder:
|
||||
|
||||
prompt = await global_prompt_manager.format_prompt(
|
||||
"heart_flow_prompt",
|
||||
structured_info=structured_info,
|
||||
chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
|
||||
if chat_in_group
|
||||
else await global_prompt_manager.get_prompt_async("chat_target_private1"),
|
||||
chat_talking_prompt=chat_talking_prompt,
|
||||
sender_name=sender_name,
|
||||
message_txt=message_txt,
|
||||
bot_name=global_config.BOT_NICKNAME,
|
||||
prompt_personality=prompt_personality,
|
||||
prompt_identity=prompt_identity,
|
||||
@@ -162,7 +173,6 @@ class PromptBuilder:
|
||||
else await global_prompt_manager.get_prompt_async("chat_target_private2"),
|
||||
current_mind_info=current_mind_info,
|
||||
reason=reason,
|
||||
keywords_reaction_prompt=keywords_reaction_prompt,
|
||||
prompt_ger=prompt_ger,
|
||||
moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
|
||||
)
|
||||
|
||||
@@ -402,3 +402,17 @@ class NormalChat:
|
||||
# 确保任务状态更新，即使等待出错 (回调函数也会尝试更新)
|
||||
if self._chat_task is task:
|
||||
self._chat_task = None
|
||||
|
||||
# 清理所有未处理的思考消息
|
||||
try:
|
||||
container = await message_manager.get_container(self.stream_id)
|
||||
if container:
|
||||
# 查找并移除所有 MessageThinking 类型的消息
|
||||
thinking_messages = [msg for msg in container.messages[:] if isinstance(msg, MessageThinking)]
|
||||
if thinking_messages:
|
||||
for msg in thinking_messages:
|
||||
container.messages.remove(msg)
|
||||
logger.info(f"[{self.stream_name}] 清理了 {len(thinking_messages)} 条未处理的思考消息。")
|
||||
except Exception as e:
|
||||
logger.error(f"[{self.stream_name}] 清理思考消息时出错: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
@@ -83,6 +83,7 @@ class NormalChatGenerator:
|
||||
build_mode="normal",
|
||||
reason="",
|
||||
current_mind_info="",
|
||||
structured_info="",
|
||||
message_txt=message.processed_plain_text,
|
||||
sender_name=sender_name,
|
||||
chat_stream=message.chat_stream,
|
||||
|
||||
Reference in New Issue
Block a user