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feat：使用action_manager统一调度action，可扩展action

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SengokuCola
2025-05-14 18:27:42 +08:00
parent e603a00a5f
commit ba85dd76a4
10 changed files with 491 additions and 345 deletions
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152
src/chat/focus_chat/planners/planner.py
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@@ -1,6 +1,6 @@
import json # <--- 确保导入 json
import traceback
from typing import List, Dict, Any
from typing import List, Dict, Any, Optional
from rich.traceback import install
from src.chat.models.utils_model import LLMRequest
from src.config.config import global_config
@@ -10,16 +10,57 @@ 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
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
from src.individuality.individuality import Individuality
from src.chat.focus_chat.planners.action_factory import ActionManager
from src.chat.focus_chat.planners.action_factory import ActionInfo
logger = get_logger("planner")
install(extra_lines=3)
def init_prompt():
Prompt(
"""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：
{chat_content_block}
{mind_info_block}
{cycle_info_block}
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:
{action_options_text}
你必须从上面列出的可用action中选择一个，并说明原因。
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。
请你以下面格式输出你选择的action：
{{
"action": "action_name",
"reasoning": "你的决策理由",
"参数1": "参数1的值",
"参数2": "参数2的值",
"参数3": "参数3的值",
...
}}
请输出你的决策 JSON：""",
"planner_prompt",)
Prompt(
"""
action_name: {action_name}
描述：{action_description}
参数：
{action_parameters}
动作要求：
{action_require}
""",
"action_prompt",
)
class ActionPlanner:
def __init__(self, log_prefix: str):
def __init__(self, log_prefix: str, action_manager: ActionManager):
self.log_prefix = log_prefix
# LLM规划器配置
self.planner_llm = LLMRequest(
@@ -27,6 +68,8 @@ class ActionPlanner:
max_tokens=1000,
request_type="action_planning", # 用于动作规划
)
self.action_manager = action_manager
async def plan(self, all_plan_info: List[InfoBase], cycle_timers: dict) -> Dict[str, Any]:
"""
@@ -62,16 +105,15 @@ class ActionPlanner:
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()
current_available_actions = self.action_manager.get_using_actions()
# --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
prompt = await prompt_builder.build_planner_prompt(
prompt = await self.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
# structured_info=structured_info, # <-- Pass SubMind info
current_available_actions=current_available_actions, # <-- Pass determined actions
cycle_info=cycle_info, # <-- Pass cycle info
)
@@ -139,9 +181,9 @@ class ActionPlanner:
)
# 恢复原始动作集
ActionFactory.restore_actions()
self.action_manager.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())}"
f"{self.log_prefix}恢复了原始动作集, 当前可用: {list(self.action_manager.get_using_actions().keys())}"
)
action_result = {"action_type": action, "action_data": action_data, "reasoning": reasoning}
@@ -154,3 +196,91 @@ class ActionPlanner:
# 返回结果字典
return plan_result
async def build_planner_prompt(
self,
is_group_chat: bool, # Now passed as argument
chat_target_info: Optional[dict], # Now passed as argument
observed_messages_str: str,
current_mind: Optional[str],
current_available_actions: Dict[str, ActionInfo],
cycle_info: Optional[str],
) -> str:
"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
try:
# --- Determine chat context ---
chat_context_description = "你现在正在一个群聊中"
chat_target_name = None # Only relevant for private
if not is_group_chat and chat_target_info:
chat_target_name = (
chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or "对方"
)
chat_context_description = f"你正在和 {chat_target_name} 私聊"
chat_content_block = ""
if observed_messages_str:
chat_content_block = f"聊天记录：\n{observed_messages_str}"
else:
chat_content_block = "你还未开始聊天"
mind_info_block = ""
if current_mind:
mind_info_block = f"对聊天的规划：{current_mind}"
else:
mind_info_block = "你刚参与聊天"
individuality = Individuality.get_instance()
personality_block = individuality.get_prompt(x_person=2, level=2)
action_options_block = ""
for using_actions_name, using_actions_info in current_available_actions.items():
# print(using_actions_name)
# print(using_actions_info)
# print(using_actions_info["parameters"])
# print(using_actions_info["require"])
# print(using_actions_info["description"])
using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt")
param_text = ""
for param_name, param_description in using_actions_info["parameters"].items():
param_text += f"{param_name}: {param_description}\n"
require_text = ""
for require_item in using_actions_info["require"]:
require_text += f"- {require_item}\n"
using_action_prompt = using_action_prompt.format(
action_name=using_actions_name,
action_description=using_actions_info["description"],
action_parameters=param_text,
action_require=require_text,
)
action_options_block += using_action_prompt
planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
prompt = planner_prompt_template.format(
bot_name=global_config.BOT_NICKNAME,
prompt_personality=personality_block,
chat_context_description=chat_context_description,
chat_content_block=chat_content_block,
mind_info_block=mind_info_block,
cycle_info_block=cycle_info,
action_options_text=action_options_block,
)
return prompt
except Exception as e:
logger.error(f"构建 Planner 提示词时出错: {e}")
logger.error(traceback.format_exc())
return "构建 Planner Prompt 时出错"
init_prompt()