mirror of https://github.com/Mai-with-u/MaiBot.git
157 lines
7.2 KiB
Python
157 lines
7.2 KiB
Python
import json # <--- 确保导入 json
|
||
import traceback
|
||
from typing import List, Dict, Any
|
||
from rich.traceback import install
|
||
from src.chat.models.utils_model import LLMRequest
|
||
from src.config.config import global_config
|
||
from src.chat.focus_chat.heartflow_prompt_builder import prompt_builder
|
||
from src.chat.focus_chat.info.info_base import InfoBase
|
||
from src.chat.focus_chat.info.obs_info import ObsInfo
|
||
from src.chat.focus_chat.info.cycle_info import CycleInfo
|
||
from src.chat.focus_chat.info.mind_info import MindInfo
|
||
from src.chat.focus_chat.info.structured_info import StructuredInfo
|
||
from src.chat.focus_chat.planners.action_factory import ActionFactory
|
||
from src.common.logger_manager import get_logger
|
||
|
||
logger = get_logger("planner")
|
||
|
||
install(extra_lines=3)
|
||
|
||
|
||
class ActionPlanner:
|
||
def __init__(self, log_prefix: str):
|
||
self.log_prefix = log_prefix
|
||
# LLM规划器配置
|
||
self.planner_llm = LLMRequest(
|
||
model=global_config.llm_plan,
|
||
max_tokens=1000,
|
||
request_type="action_planning", # 用于动作规划
|
||
)
|
||
|
||
async def plan(self, all_plan_info: List[InfoBase], cycle_timers: dict) -> Dict[str, Any]:
|
||
"""
|
||
规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
|
||
|
||
参数:
|
||
all_plan_info: 所有计划信息
|
||
cycle_timers: 计时器字典
|
||
"""
|
||
|
||
action = "no_reply" # 默认动作
|
||
reasoning = "规划器初始化默认"
|
||
|
||
try:
|
||
# 获取观察信息
|
||
for info in all_plan_info:
|
||
if isinstance(info, ObsInfo):
|
||
logger.debug(f"{self.log_prefix} 观察信息: {info}")
|
||
observed_messages = info.get_talking_message()
|
||
observed_messages_str = info.get_talking_message_str_truncate()
|
||
chat_type = info.get_chat_type()
|
||
if chat_type == "group":
|
||
is_group_chat = True
|
||
else:
|
||
is_group_chat = False
|
||
elif isinstance(info, MindInfo):
|
||
logger.debug(f"{self.log_prefix} 思维信息: {info}")
|
||
current_mind = info.get_current_mind()
|
||
elif isinstance(info, CycleInfo):
|
||
logger.debug(f"{self.log_prefix} 循环信息: {info}")
|
||
cycle_info = info.get_observe_info()
|
||
elif isinstance(info, StructuredInfo):
|
||
logger.debug(f"{self.log_prefix} 结构化信息: {info}")
|
||
structured_info = info.get_data()
|
||
|
||
# 获取我们将传递给 prompt 构建器和用于验证的当前可用动作
|
||
current_available_actions = ActionFactory.get_available_actions()
|
||
|
||
# --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
|
||
prompt = await prompt_builder.build_planner_prompt(
|
||
is_group_chat=is_group_chat, # <-- Pass HFC state
|
||
chat_target_info=None,
|
||
observed_messages_str=observed_messages_str, # <-- Pass local variable
|
||
current_mind=current_mind, # <-- Pass argument
|
||
structured_info=structured_info, # <-- Pass SubMind info
|
||
current_available_actions=current_available_actions, # <-- Pass determined actions
|
||
cycle_info=cycle_info, # <-- Pass cycle info
|
||
)
|
||
|
||
# --- 调用 LLM (普通文本生成) ---
|
||
llm_content = None
|
||
try:
|
||
llm_content, _, _ = await self.planner_llm.generate_response(prompt=prompt)
|
||
logger.debug(f"{self.log_prefix}[Planner] LLM 原始 JSON 响应 (预期): {llm_content}")
|
||
except Exception as req_e:
|
||
logger.error(f"{self.log_prefix}[Planner] LLM 请求执行失败: {req_e}")
|
||
reasoning = f"LLM 请求失败，你的模型出现问题: {req_e}"
|
||
action = "no_reply"
|
||
|
||
if llm_content:
|
||
try:
|
||
# 尝试去除可能的 markdown 代码块标记
|
||
cleaned_content = (
|
||
llm_content.strip().removeprefix("```json").removeprefix("```").removesuffix("```").strip()
|
||
)
|
||
if not cleaned_content:
|
||
raise json.JSONDecodeError("Cleaned content is empty", cleaned_content, 0)
|
||
parsed_json = json.loads(cleaned_content)
|
||
|
||
# 提取决策，提供默认值
|
||
extracted_action = parsed_json.get("action", "no_reply")
|
||
extracted_reasoning = parsed_json.get("reasoning", "LLM未提供理由")
|
||
|
||
# 新的reply格式
|
||
if extracted_action == "reply":
|
||
action_data = {
|
||
"text": parsed_json.get("text", []),
|
||
"emojis": parsed_json.get("emojis", []),
|
||
"target": parsed_json.get("target", ""),
|
||
}
|
||
else:
|
||
action_data = {} # 其他动作可能不需要额外数据
|
||
|
||
if extracted_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: '{extracted_action}' (可用: {list(current_available_actions.keys())})，将强制使用 'no_reply'"
|
||
)
|
||
action = "no_reply"
|
||
reasoning = f"LLM 返回了当前不可用的动作 '{extracted_action}' (可用: {list(current_available_actions.keys())})。原始理由: {extracted_reasoning}"
|
||
else:
|
||
# 动作有效且可用
|
||
action = extracted_action
|
||
reasoning = extracted_reasoning
|
||
|
||
except Exception as json_e:
|
||
logger.warning(
|
||
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}'"
|
||
)
|
||
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" # 发生未知错误，标记为 error 动作
|
||
reasoning = f"Planner 内部处理错误: {outer_e}"
|
||
|
||
logger.debug(
|
||
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}"
|
||
)
|
||
|
||
# 恢复原始动作集
|
||
ActionFactory.restore_actions()
|
||
logger.debug(
|
||
f"{self.log_prefix}恢复了原始动作集, 当前可用: {list(ActionFactory.get_available_actions().keys())}"
|
||
)
|
||
|
||
action_result = {"action_type": action, "action_data": action_data, "reasoning": reasoning}
|
||
|
||
plan_result = {
|
||
"action_result": action_result,
|
||
"current_mind": current_mind,
|
||
"observed_messages": observed_messages,
|
||
}
|
||
|
||
# 返回结果字典
|
||
return plan_result
|