feat：拆分HFC组件，为插件做准备
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SengokuCola
156
src/chat/focus_chat/planners/planner.py
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156
src/chat/focus_chat/planners/planner.py
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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
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from rich.traceback import install
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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.chat.focus_chat.planners.action_factory import ActionFactory
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from src.common.logger_manager import get_logger
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logger = get_logger("planner")
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install(extra_lines=3)
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class ActionPlanner:
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def __init__(self, log_prefix: str):
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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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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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# 获取我们将传递给 prompt 构建器和用于验证的当前可用动作
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current_available_actions = ActionFactory.get_available_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 prompt_builder.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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ActionFactory.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(ActionFactory.get_available_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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