mirror of https://github.com/Mai-with-u/MaiBot.git
304 lines
13 KiB
Python
304 lines
13 KiB
Python
import time
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import json # <--- 确保导入 json
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import traceback
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from typing import List, Dict, Any, Optional
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from rich.traceback import install
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from src.chat.message_receive.chat_stream import ChatStream
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from src.chat.models.utils_model import LLMRequest
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from src.config.config import global_config
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from src.chat.focus_chat.heartflow_prompt_builder import prompt_builder
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from src.chat.focus_chat.info.info_base import InfoBase
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from src.chat.focus_chat.info.obs_info import ObsInfo
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from src.chat.focus_chat.info.cycle_info import CycleInfo
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from src.chat.focus_chat.info.mind_info import MindInfo
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from src.chat.focus_chat.info.structured_info import StructuredInfo
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from src.common.logger_manager import get_logger
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.individuality.individuality import Individuality
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from src.chat.focus_chat.planners.action_factory import ActionManager
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from src.chat.focus_chat.planners.action_factory import ActionInfo
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from src.chat.utils.chat_message_builder import get_raw_msg_before_timestamp_with_chat
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from src.plugins.group_nickname.nickname_manager import nickname_manager
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logger = get_logger("planner")
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install(extra_lines=3)
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def init_prompt():
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Prompt(
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"""你的名字是{bot_name},{prompt_personality}，{chat_context_description}。需要基于以下信息决定如何参与对话：
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{nickname_info_block}
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{chat_content_block}
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{mind_info_block}
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{cycle_info_block}
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请综合分析聊天内容和你看到的新消息，参考聊天规划，选择合适的action:
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{action_options_text}
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你必须从上面列出的可用action中选择一个，并说明原因。
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你的决策必须以严格的 JSON 格式输出，且仅包含 JSON 内容，不要有任何其他文字或解释。
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请你以下面格式输出你选择的action：
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{{
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"action": "action_name",
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"reasoning": "你的决策理由",
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"参数1": "参数1的值",
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"参数2": "参数2的值",
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"参数3": "参数3的值",
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...
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}}
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请输出你的决策 JSON：""",
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"planner_prompt",)
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Prompt(
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"""
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action_name: {action_name}
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描述：{action_description}
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参数：
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{action_parameters}
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动作要求：
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{action_require}
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""",
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"action_prompt",
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)
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class ActionPlanner:
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def __init__(self, log_prefix: str, action_manager: ActionManager, stream_id: str, chat_stream: ChatStream):
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self.log_prefix = log_prefix
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# LLM规划器配置
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self.planner_llm = LLMRequest(
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model=global_config.llm_plan,
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max_tokens=1000,
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request_type="action_planning", # 用于动作规划
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)
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self.action_manager = action_manager
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self.stream_id = stream_id
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self.chat_stream = chat_stream
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async def plan(self, all_plan_info: List[InfoBase], cycle_timers: dict) -> 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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参数:
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all_plan_info: 所有计划信息
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cycle_timers: 计时器字典
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"""
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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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try:
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# 获取观察信息
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for info in all_plan_info:
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if isinstance(info, ObsInfo):
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logger.debug(f"{self.log_prefix} 观察信息: {info}")
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observed_messages = info.get_talking_message()
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observed_messages_str = info.get_talking_message_str_truncate()
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chat_type = info.get_chat_type()
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if chat_type == "group":
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is_group_chat = True
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else:
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is_group_chat = False
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elif isinstance(info, MindInfo):
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logger.debug(f"{self.log_prefix} 思维信息: {info}")
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current_mind = info.get_current_mind()
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elif isinstance(info, CycleInfo):
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logger.debug(f"{self.log_prefix} 循环信息: {info}")
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cycle_info = info.get_observe_info()
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elif isinstance(info, StructuredInfo):
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logger.debug(f"{self.log_prefix} 结构化信息: {info}")
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structured_info = info.get_data()
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current_available_actions = self.action_manager.get_using_actions()
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# --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
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prompt = await self.build_planner_prompt(
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is_group_chat=is_group_chat, # <-- Pass HFC state
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chat_target_info=None,
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observed_messages_str=observed_messages_str, # <-- Pass local variable
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current_mind=current_mind, # <-- Pass argument
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# structured_info=structured_info, # <-- Pass SubMind info
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current_available_actions=current_available_actions, # <-- Pass determined actions
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cycle_info=cycle_info, # <-- Pass cycle info
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)
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# --- 调用 LLM (普通文本生成) ---
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llm_content = None
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try:
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llm_content, _, _ = await self.planner_llm.generate_response(prompt=prompt)
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logger.debug(f"{self.log_prefix}[Planner] LLM 原始 JSON 响应 (预期): {llm_content}")
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except Exception as req_e:
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logger.error(f"{self.log_prefix}[Planner] LLM 请求执行失败: {req_e}")
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reasoning = f"LLM 请求失败，你的模型出现问题: {req_e}"
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action = "no_reply"
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if llm_content:
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try:
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# 尝试去除可能的 markdown 代码块标记
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cleaned_content = (
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llm_content.strip().removeprefix("```json").removeprefix("```").removesuffix("```").strip()
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)
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if not cleaned_content:
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raise json.JSONDecodeError("Cleaned content is empty", cleaned_content, 0)
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parsed_json = json.loads(cleaned_content)
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# 提取决策，提供默认值
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extracted_action = parsed_json.get("action", "no_reply")
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extracted_reasoning = parsed_json.get("reasoning", "LLM未提供理由")
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# 新的reply格式
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if extracted_action == "reply":
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action_data = {
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"text": parsed_json.get("text", []),
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"emojis": parsed_json.get("emojis", []),
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"target": parsed_json.get("target", ""),
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}
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else:
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action_data = {} # 其他动作可能不需要额外数据
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if extracted_action not in current_available_actions:
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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: '{extracted_action}' (可用: {list(current_available_actions.keys())})，将强制使用 'no_reply'"
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)
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action = "no_reply"
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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}"
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else:
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# 动作有效且可用
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action = extracted_action
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reasoning = extracted_reasoning
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except Exception as json_e:
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logger.warning(
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f"{self.log_prefix}解析LLM响应JSON失败，模型返回不标准: {json_e}. LLM原始输出: '{llm_content}'"
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)
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reasoning = f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_reply'."
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action = "no_reply"
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except Exception as outer_e:
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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}")
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traceback.print_exc()
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action = "no_reply" # 发生未知错误，标记为 error 动作
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reasoning = f"Planner 内部处理错误: {outer_e}"
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logger.debug(
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f"{self.log_prefix}规划器Prompt:\n{prompt}\n\n决策动作:{action},\n动作信息: '{action_data}'\n理由: {reasoning}"
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)
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# 恢复原始动作集
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self.action_manager.restore_actions()
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logger.debug(
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f"{self.log_prefix}恢复了原始动作集, 当前可用: {list(self.action_manager.get_using_actions().keys())}"
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)
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action_result = {"action_type": action, "action_data": action_data, "reasoning": reasoning}
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plan_result = {
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"action_result": action_result,
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"current_mind": current_mind,
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"observed_messages": observed_messages,
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}
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# 返回结果字典
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return plan_result
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async def build_planner_prompt(
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self,
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is_group_chat: bool, # Now passed as argument
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chat_target_info: Optional[dict], # Now passed as argument
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observed_messages_str: str,
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current_mind: Optional[str],
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current_available_actions: Dict[str, ActionInfo],
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cycle_info: Optional[str],
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) -> str:
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"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
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try:
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# --- Determine chat context ---
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chat_context_description = "你现在正在一个群聊中"
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chat_target_name = None # Only relevant for private
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if not is_group_chat and chat_target_info:
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chat_target_name = (
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chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or "对方"
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)
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chat_context_description = f"你正在和 {chat_target_name} 私聊"
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chat_content_block = ""
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if observed_messages_str:
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chat_content_block = f"聊天记录：\n{observed_messages_str}"
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else:
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chat_content_block = "你还未开始聊天"
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mind_info_block = ""
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if current_mind:
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mind_info_block = f"对聊天的规划：{current_mind}"
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else:
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mind_info_block = "你刚参与聊天"
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individuality = Individuality.get_instance()
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personality_block = individuality.get_prompt(x_person=2, level=2)
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action_options_block = ""
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for using_actions_name, using_actions_info in current_available_actions.items():
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# print(using_actions_name)
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# print(using_actions_info)
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# print(using_actions_info["parameters"])
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# print(using_actions_info["require"])
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# print(using_actions_info["description"])
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using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt")
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param_text = ""
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for param_name, param_description in using_actions_info["parameters"].items():
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param_text += f"{param_name}: {param_description}\n"
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require_text = ""
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for require_item in using_actions_info["require"]:
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require_text += f"- {require_item}\n"
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using_action_prompt = 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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action_options_block += using_action_prompt
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# 需要获取用于上下文的历史消息
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message_list_before_now = get_raw_msg_before_timestamp_with_chat(
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chat_id=self.stream_id,
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timestamp=time.time(), # 使用当前时间作为参考点
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limit=global_config.observation_context_size, # 使用与 prompt 构建一致的 limit
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)
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# 调用工具函数获取格式化后的绰号字符串
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nickname_injection_str = await nickname_manager.get_nickname_prompt_injection(
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self.chat_stream, message_list_before_now
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)
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planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
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prompt = planner_prompt_template.format(
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bot_name=global_config.BOT_NICKNAME,
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prompt_personality=personality_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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mind_info_block=mind_info_block,
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cycle_info_block=cycle_info,
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action_options_text=action_options_block,
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nickname_info_block=nickname_injection_str,
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)
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return prompt
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except Exception as e:
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logger.error(f"构建 Planner 提示词时出错: {e}")
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logger.error(traceback.format_exc())
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return "构建 Planner Prompt 时出错"
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init_prompt()
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