refactor(chat): 重构planner为大脑/小脑并行架构以提升性能和可扩展性(别管能不能用先插进来再说)
将原有的单体`plan`方法重构为一个多智能体架构，包含一个"大脑"和多个并行的"小脑"。 大脑 (`plan`方法) 专注于决定是否进行聊天回复 (`reply`)，并负责调度和整合所有决策。 小脑 (`sub_plan`方法) 并行处理具体的、独立的action判断。每个小脑接收一部分action，使用轻量级模型进行快速评估，从而实现并行化处理，减少了单一LLM调用的延迟。 这种新架构的主要优势包括： - **性能提升**：通过并行化action判断，显著减少了规划器的总响应时间。 - **可扩展性**：添加新的action变得更加容易，因为它们可以被分配到不同的小脑中，而不会增加主规划流程的复杂性。 - **鲁棒性**：将复杂的规划任务分解为更小的、独立的单元，降低了单个点失败导致整个规划失败的风险。 - **成本效益**：允许为小脑配置更轻量、更快速的模型，优化了资源使用。
This commit is contained in:
minecraft1024a
@@ -1,7 +1,11 @@
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import orjson
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import time
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import traceback
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from typing import Dict, Any, Optional, Tuple, List
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import asyncio
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import math
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import random
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import json
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from typing import Dict, Any, Optional, Tuple, List, TYPE_CHECKING
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from rich.traceback import install
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from datetime import datetime
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from json_repair import repair_json
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@@ -19,12 +23,15 @@ from src.chat.utils.chat_message_builder import (
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from src.chat.utils.utils import get_chat_type_and_target_info
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from src.chat.planner_actions.action_manager import ActionManager
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from src.chat.message_receive.chat_stream import get_chat_manager
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from src.plugin_system.base.component_types import ActionInfo, ChatMode, ComponentType
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from src.plugin_system.base.component_types import ActionInfo, ChatMode, ComponentType, ActionActivationType
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from src.plugin_system.core.component_registry import component_registry
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from src.schedule.schedule_manager import schedule_manager
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from src.mood.mood_manager import mood_manager
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from src.chat.memory_system.Hippocampus import hippocampus_manager
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if TYPE_CHECKING:
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pass
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logger = get_logger("planner")
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install(extra_lines=3)
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@@ -110,6 +117,37 @@ def init_prompt():
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"action_prompt",
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)
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Prompt(
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"""
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{name_block}
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{chat_context_description}，{time_block}，现在请你根据以下聊天内容，选择一个或多个合适的action。如果没有合适的action，请选择no_action。,
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{chat_content_block}
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**要求**
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1.action必须符合使用条件，如果符合条件，就选择
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2.如果聊天内容不适合使用action，即使符合条件，也不要使用
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3.{moderation_prompt}
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4.请注意如果相同的内容已经被执行，请不要重复执行
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这是你最近执行过的动作:
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{actions_before_now_block}
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**可用的action**
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no_action：不选择任何动作
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{{
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"action": "no_action",
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"reason":"不动作的原因"
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}}
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{action_options_text}
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请选择，并说明触发action的消息id和选择该action的原因。消息id格式:m+数字
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请根据动作示例，以严格的 JSON 格式输出，且仅包含 JSON 内容：
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""",
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"sub_planner_prompt",
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)
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class ActionPlanner:
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def __init__(self, chat_id: str, action_manager: ActionManager):
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@@ -117,14 +155,17 @@ class ActionPlanner:
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self.log_prefix = f"[{get_chat_manager().get_stream_name(chat_id) or chat_id}]"
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self.action_manager = action_manager
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# LLM规划器配置
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# --- 大脑 ---
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self.planner_llm = LLMRequest(
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model_set=model_config.model_task_config.planner, request_type="planner"
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) # 用于动作规划
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)
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# --- 小脑 (新增) ---
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# TODO: 可以在 model_config.toml 中为 planner_small 单独配置一个轻量级模型
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self.planner_small_llm = LLMRequest(
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model_set=model_config.model_task_config.planner, request_type="planner_small"
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)
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self.last_obs_time_mark = 0.0
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# 添加重试计数器
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self.plan_retry_count = 0
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self.max_plan_retries = 3
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async def _get_long_term_memory_context(self) -> str:
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"""
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@@ -237,6 +278,168 @@ class ActionPlanner:
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# 假设消息列表是按时间顺序排列的，最后一个是最新的
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return message_id_list[-1].get("message")
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def _parse_single_action(
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self,
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action_json: dict,
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message_id_list: list, # 使用 planner.py 的 list of dict
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current_available_actions: list, # 使用 planner.py 的 list of tuple
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) -> List[Dict[str, Any]]:
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"""
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[注释] 解析单个小脑LLM返回的action JSON，并将其转换为标准化的字典。
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"""
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parsed_actions = []
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try:
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action = action_json.get("action", "no_action")
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reasoning = action_json.get("reason", "未提供原因")
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action_data = {k: v for k, v in action_json.items() if k not in ["action", "reason"]}
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target_message = None
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if action != "no_action":
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if target_message_id := action_json.get("target_message_id"):
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target_message = self.find_message_by_id(target_message_id, message_id_list)
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if target_message is None:
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logger.warning(f"{self.log_prefix}无法找到target_message_id '{target_message_id}'")
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target_message = self.get_latest_message(message_id_list)
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else:
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logger.warning(f"{self.log_prefix}动作'{action}'缺少target_message_id")
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available_action_names = [name for name, _ in current_available_actions]
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if action not in ["no_action", "reply"] and action not in available_action_names:
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logger.warning(
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f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (可用: {available_action_names})，将强制使用 'no_action'"
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)
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reasoning = f"LLM 返回了当前不可用的动作 '{action}' (可用: {available_action_names})。原始理由: {reasoning}"
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action = "no_action"
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# 将列表转换为字典格式以供将来使用
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available_actions_dict = dict(current_available_actions)
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parsed_actions.append(
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{
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"action_type": action,
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"reasoning": reasoning,
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"action_data": action_data,
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"action_message": target_message,
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"available_actions": available_actions_dict,
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}
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)
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except Exception as e:
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logger.error(f"{self.log_prefix}解析单个action时出错: {e}")
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parsed_actions.append(
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{
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"action_type": "no_action",
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"reasoning": f"解析action时出错: {e}",
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"action_data": {},
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"action_message": None,
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"available_actions": dict(current_available_actions),
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}
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)
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return parsed_actions
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def _filter_no_actions(self, action_list: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""
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[注释] 从一个action字典列表中过滤掉所有的 'no_action'。
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如果过滤后列表为空, 则返回一个空的列表, 或者根据需要返回一个默认的no_action字典。
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"""
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non_no_actions = [a for a in action_list if a.get("action_type") not in ["no_action", "no_reply"]]
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if non_no_actions:
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return non_no_actions
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# 如果都是 no_action，则返回一个包含第一个 no_action 的列表，以保留 reason
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return action_list[:1] if action_list else []
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async def sub_plan(
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self,
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action_list: list, # 使用 planner.py 的 list of tuple
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chat_content_block: str,
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message_id_list: list, # 使用 planner.py 的 list of dict
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is_group_chat: bool = False,
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chat_target_info: Optional[dict] = None,
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) -> List[Dict[str, Any]]:
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"""
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[注释] "小脑"规划器。接收一小组actions，使用轻量级LLM判断其中哪些应该被触发。
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这是一个独立的、并行的思考单元。返回一个包含action字典的列表。
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"""
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try:
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actions_before_now = get_actions_by_timestamp_with_chat(
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chat_id=self.chat_id,
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timestamp_start=time.time() - 1200,
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timestamp_end=time.time(),
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limit=20,
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)
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action_names_in_list = [name for name, _ in action_list]
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filtered_actions = [
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record for record in actions_before_now if record.get("action_name") in action_names_in_list
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]
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actions_before_now_block = build_readable_actions(actions=filtered_actions)
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chat_context_description = "你现在正在一个群聊中"
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if not is_group_chat and chat_target_info:
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chat_target_name = chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or "对方"
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chat_context_description = f"你正在和 {chat_target_name} 私聊"
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action_options_block = ""
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for using_actions_name, using_actions_info in action_list:
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param_text = ""
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if using_actions_info.action_parameters:
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param_text = "\n" + "\n".join(
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f' "{p_name}":"{p_desc}"'
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for p_name, p_desc in using_actions_info.action_parameters.items()
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)
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require_text = "\n".join(f"- {req}" for req in using_actions_info.action_require)
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using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt")
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action_options_block += using_action_prompt.format(
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action_name=using_actions_name,
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action_description=using_actions_info.description,
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action_parameters=param_text,
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action_require=require_text,
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)
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moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
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time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
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bot_name = global_config.bot.nickname
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bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
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name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"
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planner_prompt_template = await global_prompt_manager.get_prompt_async("sub_planner_prompt")
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prompt = planner_prompt_template.format(
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time_block=time_block,
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chat_context_description=chat_context_description,
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chat_content_block=chat_content_block,
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actions_before_now_block=actions_before_now_block,
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action_options_text=action_options_block,
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moderation_prompt=moderation_prompt_block,
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name_block=name_block,
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)
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except Exception as e:
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logger.error(f"构建小脑提示词时出错: {e}\n{traceback.format_exc()}")
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return [{"action_type": "no_action", "reasoning": f"构建小脑Prompt时出错: {e}"}]
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action_dicts: List[Dict[str, Any]] = []
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try:
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llm_content, (reasoning_content, _, _) = await self.planner_small_llm.generate_response_async(prompt=prompt)
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if global_config.debug.show_prompt:
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logger.info(f"{self.log_prefix}小脑原始提示词: {prompt}")
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logger.info(f"{self.log_prefix}小脑原始响应: {llm_content}")
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else:
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logger.debug(f"{self.log_prefix}小脑原始响应: {llm_content}")
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if llm_content:
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parsed_json = orjson.loads(repair_json(llm_content))
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if isinstance(parsed_json, list):
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for item in parsed_json:
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if isinstance(item, dict):
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action_dicts.extend(self._parse_single_action(item, message_id_list, action_list))
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elif isinstance(parsed_json, dict):
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action_dicts.extend(self._parse_single_action(parsed_json, message_id_list, action_list))
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except Exception as e:
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logger.warning(f"{self.log_prefix}解析小脑响应JSON失败: {e}. LLM原始输出: '{llm_content}'")
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action_dicts.append({"action_type": "no_action", "reasoning": f"解析小脑响应失败: {e}"})
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if not action_dicts:
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action_dicts.append({"action_type": "no_action", "reasoning": "小脑未返回有效action"})
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return action_dicts
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async def plan(
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self,
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mode: ChatMode = ChatMode.FOCUS,
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@@ -244,153 +447,180 @@ class ActionPlanner:
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available_actions: Optional[Dict[str, ActionInfo]] = None,
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) -> Tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]]:
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"""
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规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
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[注释] "大脑"规划器。
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1. 启动多个并行的"小脑"(sub_plan)来决定是否执行具体的actions。
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2. 自己(大脑)则专注于决定是否进行聊天回复(reply)。
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3. 整合大脑和小脑的决策，返回最终要执行的动作列表。
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"""
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# --- 1. 准备上下文信息 ---
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message_list_before_now = get_raw_msg_before_timestamp_with_chat(
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chat_id=self.chat_id,
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timestamp=time.time(),
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limit=int(global_config.chat.max_context_size * 0.6),
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)
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# 大脑使用较长的上下文
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chat_content_block, message_id_list = build_readable_messages_with_id(
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messages=message_list_before_now,
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timestamp_mode="normal",
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read_mark=self.last_obs_time_mark,
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truncate=True,
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show_actions=True,
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)
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# 小脑使用较短、较新的上下文
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message_list_before_now_short = message_list_before_now[-int(global_config.chat.max_context_size * 0.3) :]
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chat_content_block_short, message_id_list_short = build_readable_messages_with_id(
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messages=message_list_before_now_short,
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timestamp_mode="normal",
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truncate=False,
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show_actions=False,
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)
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self.last_obs_time_mark = time.time()
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action = "no_reply" # 默认动作
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reasoning = "规划器初始化默认"
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action_data = {}
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current_available_actions: Dict[str, ActionInfo] = {}
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target_message: Optional[Dict[str, Any]] = None # 初始化target_message变量
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prompt: str = ""
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message_id_list: list = []
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is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info()
|
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if available_actions is None:
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available_actions = current_available_actions
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|
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# --- 2. 启动小脑并行思考 ---
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all_sub_planner_results: List[Dict[str, Any]] = []
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try:
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is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info()
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sub_planner_actions: Dict[str, ActionInfo] = {}
|
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for action_name, action_info in available_actions.items():
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if action_info.activation_type in [ActionActivationType.LLM_JUDGE, ActionActivationType.ALWAYS]:
|
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sub_planner_actions[action_name] = action_info
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elif action_info.activation_type == ActionActivationType.RANDOM:
|
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if random.random() < action_info.random_activation_probability:
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sub_planner_actions[action_name] = action_info
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elif action_info.activation_type == ActionActivationType.KEYWORD:
|
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if any(keyword in chat_content_block_short for keyword in action_info.activation_keywords):
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sub_planner_actions[action_name] = action_info
|
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|
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# --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
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prompt, message_id_list = await self.build_planner_prompt(
|
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is_group_chat=is_group_chat, # <-- Pass HFC state
|
||||
chat_target_info=chat_target_info, # <-- 传递获取到的聊天目标信息
|
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current_available_actions=current_available_actions, # <-- Pass determined actions
|
||||
if sub_planner_actions:
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sub_planner_actions_num = len(sub_planner_actions)
|
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# TODO: 您可以在 config.toml 的 [chat] 部分添加 planner_size = 5.0 来自定义此值
|
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planner_size_config = getattr(global_config.chat, "planner_size", 5.0)
|
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sub_planner_size = int(planner_size_config) + (
|
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1 if random.random() < planner_size_config - int(planner_size_config) else 0
|
||||
)
|
||||
sub_planner_num = math.ceil(sub_planner_actions_num / sub_planner_size)
|
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logger.info(f"{self.log_prefix}使用{sub_planner_num}个小脑进行思考 (尺寸: {sub_planner_size})")
|
||||
|
||||
action_items = list(sub_planner_actions.items())
|
||||
random.shuffle(action_items)
|
||||
sub_planner_lists = [action_items[i::sub_planner_num] for i in range(sub_planner_num)]
|
||||
|
||||
sub_plan_tasks = [
|
||||
self.sub_plan(
|
||||
action_list=action_group,
|
||||
chat_content_block=chat_content_block_short,
|
||||
message_id_list=message_id_list_short,
|
||||
is_group_chat=is_group_chat,
|
||||
chat_target_info=chat_target_info,
|
||||
)
|
||||
for action_group in sub_planner_lists
|
||||
]
|
||||
sub_plan_results = await asyncio.gather(*sub_plan_tasks)
|
||||
for sub_result in sub_plan_results:
|
||||
all_sub_planner_results.extend(sub_result)
|
||||
|
||||
sub_actions_str = ", ".join(
|
||||
a["action_type"] for a in all_sub_planner_results if a["action_type"] != "no_action"
|
||||
) or "no_action"
|
||||
logger.info(f"{self.log_prefix}小脑决策: [{sub_actions_str}]")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}小脑调度过程中出错: {e}\n{traceback.format_exc()}")
|
||||
|
||||
# --- 3. 大脑独立思考是否回复 ---
|
||||
action, reasoning, action_data, target_message = "no_reply", "大脑初始化默认", {}, None
|
||||
try:
|
||||
prompt, _ = await self.build_planner_prompt(
|
||||
is_group_chat=is_group_chat,
|
||||
chat_target_info=chat_target_info,
|
||||
current_available_actions={}, # 大脑不考虑具体action
|
||||
mode=mode,
|
||||
chat_content_block_override=chat_content_block,
|
||||
message_id_list_override=message_id_list,
|
||||
)
|
||||
|
||||
# --- 调用 LLM (普通文本生成) ---
|
||||
llm_content = None
|
||||
try:
|
||||
llm_content, (reasoning_content, _, _) = await self.planner_llm.generate_response_async(prompt=prompt)
|
||||
|
||||
if global_config.debug.show_prompt:
|
||||
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
|
||||
logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||||
if reasoning_content:
|
||||
logger.info(f"{self.log_prefix}规划器推理: {reasoning_content}")
|
||||
else:
|
||||
logger.debug(f"{self.log_prefix}规划器原始提示词: {prompt}")
|
||||
logger.debug(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||||
if reasoning_content:
|
||||
logger.debug(f"{self.log_prefix}规划器推理: {reasoning_content}")
|
||||
|
||||
except Exception as req_e:
|
||||
logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")
|
||||
reasoning = f"LLM 请求失败，模型出现问题: {req_e}"
|
||||
action = "no_reply"
|
||||
llm_content, _ = await self.planner_llm.generate_response_async(prompt=prompt)
|
||||
|
||||
if llm_content:
|
||||
try:
|
||||
parsed_json = orjson.loads(repair_json(llm_content))
|
||||
|
||||
if isinstance(parsed_json, list):
|
||||
if parsed_json:
|
||||
parsed_json = parsed_json[-1]
|
||||
logger.warning(f"{self.log_prefix}LLM返回了多个JSON对象，使用最后一个: {parsed_json}")
|
||||
else:
|
||||
parsed_json = {}
|
||||
|
||||
if not isinstance(parsed_json, dict):
|
||||
logger.error(f"{self.log_prefix}解析后的JSON不是字典类型: {type(parsed_json)}")
|
||||
parsed_json = {}
|
||||
|
||||
parsed_json = orjson.loads(repair_json(llm_content))
|
||||
parsed_json = parsed_json[-1] if isinstance(parsed_json, list) and parsed_json else parsed_json
|
||||
if isinstance(parsed_json, dict):
|
||||
action = parsed_json.get("action", "no_reply")
|
||||
reasoning = parsed_json.get("reason", "未提供原因")
|
||||
|
||||
# 将所有其他属性添加到action_data
|
||||
for key, value in parsed_json.items():
|
||||
if key not in ["action", "reason"]:
|
||||
action_data[key] = value
|
||||
|
||||
# 非no_reply动作需要target_message_id
|
||||
action_data = {k: v for k, v in parsed_json.items() if k not in ["action", "reason"]}
|
||||
if action != "no_reply":
|
||||
if target_message_id := parsed_json.get("target_message_id"):
|
||||
# 根据target_message_id查找原始消息
|
||||
target_message = self.find_message_by_id(target_message_id, message_id_list)
|
||||
# 如果获取的target_message为None，输出warning并重新plan
|
||||
if target_message is None:
|
||||
self.plan_retry_count += 1
|
||||
logger.warning(
|
||||
f"{self.log_prefix}无法找到target_message_id '{target_message_id}' 对应的消息，重试次数: {self.plan_retry_count}/{self.max_plan_retries}"
|
||||
)
|
||||
if target_id := parsed_json.get("target_message_id"):
|
||||
target_message = self.find_message_by_id(target_id, message_id_list)
|
||||
if not target_message:
|
||||
target_message = self.get_latest_message(message_id_list)
|
||||
logger.info(f"{self.log_prefix}大脑决策: [{action}]")
|
||||
|
||||
# 如果连续三次plan均为None，输出error并选取最新消息
|
||||
if self.plan_retry_count >= self.max_plan_retries:
|
||||
logger.error(
|
||||
f"{self.log_prefix}连续{self.max_plan_retries}次plan获取target_message失败，选择最新消息作为target_message"
|
||||
)
|
||||
target_message = self.get_latest_message(message_id_list)
|
||||
self.plan_retry_count = 0 # 重置计数器
|
||||
else:
|
||||
# 递归重新plan
|
||||
return await self.plan(mode, loop_start_time, available_actions)
|
||||
else:
|
||||
# 成功获取到target_message，重置计数器
|
||||
self.plan_retry_count = 0
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix}动作'{action}'缺少target_message_id")
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix}大脑处理过程中发生意外错误: {e}\n{traceback.format_exc()}")
|
||||
action, reasoning = "no_reply", f"大脑处理错误: {e}"
|
||||
|
||||
if action != "no_reply" and action != "reply" and action not in current_available_actions:
|
||||
logger.warning(
|
||||
f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (可用: {list(current_available_actions.keys())})，将强制使用 'no_reply'"
|
||||
)
|
||||
reasoning = f"LLM 返回了当前不可用的动作 '{action}' (可用: {list(current_available_actions.keys())})。原始理由: {reasoning}"
|
||||
action = "no_reply"
|
||||
|
||||
except Exception as json_e:
|
||||
logger.warning(f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'")
|
||||
traceback.print_exc()
|
||||
reasoning = f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_reply'."
|
||||
action = "no_reply"
|
||||
|
||||
except Exception as outer_e:
|
||||
logger.error(f"{self.log_prefix}Planner 处理过程中发生意外错误，规划失败，将执行 no_reply: {outer_e}")
|
||||
traceback.print_exc()
|
||||
action = "no_reply"
|
||||
reasoning = f"Planner 内部处理错误: {outer_e}"
|
||||
|
||||
is_parallel = False
|
||||
if mode == ChatMode.NORMAL and action in current_available_actions:
|
||||
is_parallel = current_available_actions[action].parallel_action
|
||||
# --- 4. 整合大脑和小脑的决策 ---
|
||||
is_parallel = True
|
||||
for info in all_sub_planner_results:
|
||||
action_type = info.get("action_type")
|
||||
if action_type and action_type not in ["no_action", "no_reply"]:
|
||||
action_info = available_actions.get(action_type)
|
||||
if action_info and not action_info.parallel_action:
|
||||
is_parallel = False
|
||||
break
|
||||
|
||||
action_data["loop_start_time"] = loop_start_time
|
||||
final_actions: List[Dict[str, Any]] = []
|
||||
|
||||
actions = []
|
||||
if is_parallel:
|
||||
logger.info(f"{self.log_prefix}决策模式: 大脑与小脑并行")
|
||||
if action not in ["no_action", "no_reply"]:
|
||||
final_actions.append(
|
||||
{
|
||||
"action_type": action,
|
||||
"reasoning": reasoning,
|
||||
"action_data": action_data,
|
||||
"action_message": target_message,
|
||||
"available_actions": available_actions,
|
||||
}
|
||||
)
|
||||
final_actions.extend(all_sub_planner_results)
|
||||
else:
|
||||
logger.info(f"{self.log_prefix}决策模式: 小脑优先 (检测到非并行action)")
|
||||
final_actions.extend(all_sub_planner_results)
|
||||
|
||||
# 1. 添加Planner取得的动作
|
||||
actions.append(
|
||||
{
|
||||
"action_type": action,
|
||||
"reasoning": reasoning,
|
||||
"action_data": action_data,
|
||||
"action_message": target_message,
|
||||
"available_actions": available_actions, # 添加这个字段
|
||||
}
|
||||
)
|
||||
final_actions = self._filter_no_actions(final_actions)
|
||||
|
||||
if action != "reply" and is_parallel:
|
||||
actions.append(
|
||||
{"action_type": "reply", "action_message": target_message, "available_actions": available_actions}
|
||||
)
|
||||
if not final_actions:
|
||||
final_actions = [
|
||||
{
|
||||
"action_type": "no_action",
|
||||
"reasoning": "所有规划器都选择不执行动作",
|
||||
"action_data": {}, "action_message": None, "available_actions": available_actions
|
||||
}
|
||||
]
|
||||
|
||||
return actions, target_message
|
||||
final_target_message = target_message
|
||||
if not final_target_message and final_actions:
|
||||
final_target_message = next((act.get("action_message") for act in final_actions if act.get("action_message")), None)
|
||||
|
||||
actions_str = ", ".join([a.get('action_type', 'N/A') for a in final_actions])
|
||||
logger.info(f"{self.log_prefix}最终执行动作 ({len(final_actions)}): [{actions_str}]")
|
||||
|
||||
return final_actions, final_target_message
|
||||
|
||||
async def build_planner_prompt(
|
||||
self,
|
||||
is_group_chat: bool, # Now passed as argument
|
||||
chat_target_info: Optional[dict], # Now passed as argument
|
||||
is_group_chat: bool,
|
||||
chat_target_info: Optional[dict],
|
||||
current_available_actions: Dict[str, ActionInfo],
|
||||
refresh_time: bool = False,
|
||||
mode: ChatMode = ChatMode.FOCUS,
|
||||
) -> tuple[str, list]: # sourcery skip: use-join
|
||||
chat_content_block_override: Optional[str] = None,
|
||||
message_id_list_override: Optional[List] = None,
|
||||
refresh_time: bool = False, # 添加缺失的参数
|
||||
) -> tuple[str, list]:
|
||||
"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
|
||||
try:
|
||||
# --- 通用信息获取 ---
|
||||
|
||||
Reference in New Issue
Block a user