e568a7e439
- 在 `DefaultReplyer` 中增加了对 `reply_message` 的空值检查，以避免在无回复上下文时引发错误。 - 在 `ActionPlanner` 中将 'do_nothing' 添加到非目标动作列表中，以正确处理此新增的无操作指令。 - 将 `EmojiAction` 使用的 LLM 模型从 'utils_small' 更新为 'planner'，以适应模型配置的变更。
700 lines
29 KiB
Python
700 lines
29 KiB
Python
import orjson
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import time
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import traceback
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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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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config, model_config
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from src.common.logger import get_logger
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from src.chat.utils.prompt import Prompt, global_prompt_manager
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from src.chat.utils.chat_message_builder import (
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build_readable_actions,
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get_actions_by_timestamp_with_chat,
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build_readable_messages_with_id,
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get_raw_msg_before_timestamp_with_chat,
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)
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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 (
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ActionInfo,
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ChatMode,
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ComponentType,
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ActionActivationType,
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)
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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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def init_prompt():
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Prompt(
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"""
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{schedule_block}
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{mood_block}
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{time_block}
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{identity_block}
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{users_in_chat}
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{custom_prompt_block}
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{chat_context_description}，以下是具体的聊天内容。
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{chat_content_block}
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{moderation_prompt}
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**任务: 构建一个完整的响应**
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你的任务是根据当前的聊天内容，构建一个完整的、人性化的响应。一个完整的响应由两部分组成：
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1. **主要动作**: 这是响应的核心，通常是 `reply`（文本回复）。
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2. **辅助动作 (可选)**: 这是为了增强表达效果的附加动作，例如 `emoji`（发送表情包）或 `poke_user`（戳一戳）。
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**决策流程:**
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1. 首先，决定是否要进行 `reply`。
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2. 然后，评估当前的对话气氛和用户情绪，判断是否需要一个**辅助动作**来让你的回应更生动、更符合你的性格。
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3. 如果需要，选择一个最合适的辅助动作与 `reply` 组合。
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4. 如果用户明确要求了某个动作，请务必优先满足。
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**可用动作:**
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{actions_before_now_block}
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{no_action_block}
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动作：reply
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动作描述：参与聊天回复，发送文本进行表达
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- 你想要闲聊或者随便附和
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- {mentioned_bonus}
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- 如果你刚刚进行了回复，不要对同一个话题重复回应
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- 不要回复自己发送的消息
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{{
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"action": "reply",
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"target_message_id": "触发action的消息id",
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"reason": "回复的原因"
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}}
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{action_options_text}
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**输出格式:**
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你必须以严格的 JSON 格式输出，返回一个包含所有选定动作的JSON列表。如果没有任何合适的动作，返回一个空列表[]。
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**单动作示例 (仅回复):**
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[
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{{
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"action": "reply",
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"target_message_id": "m123",
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"reason": "回答用户的问题"
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}}
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]
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**组合动作示例 (回复 + 表情包):**
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[
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{{
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"action": "reply",
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"target_message_id": "m123",
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"reason": "回答用户的问题"
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}},
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{{
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"action": "emoji",
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"target_message_id": "m123",
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"reason": "用一个可爱的表情来缓和气氛"
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}}
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]
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不要输出markdown格式```json等内容，直接输出且仅包含 JSON 列表内容：
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""",
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"planner_prompt",
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)
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Prompt(
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"""
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# 主动思考决策
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## 你的内部状态
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{time_block}
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{identity_block}
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{schedule_block}
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{mood_block}
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## 长期记忆摘要
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{long_term_memory_block}
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## 最近的聊天内容
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{chat_content_block}
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## 最近的动作历史
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{actions_before_now_block}
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## 任务
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你现在要决定是否主动说些什么。就像一个真实的人一样，有时候会突然想起之前聊到的话题，或者对朋友的近况感到好奇，想主动询问或关心一下。
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请基于聊天内容，用你的判断力来决定是否要主动发言。不要按照固定规则，而是像人类一样自然地思考：
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- 是否想起了什么之前提到的事情，想问问后来怎么样了？
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- 是否注意到朋友提到了什么值得关心的事情？
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- 是否有什么话题突然想到，觉得现在聊聊很合适？
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- 或者觉得现在保持沉默更好？
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## 可用动作
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动作：proactive_reply
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动作描述：主动发起对话，可以是关心朋友、询问近况、延续之前的话题，或分享想法。
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- 当你突然想起之前的话题，想询问进展时
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- 当你想关心朋友的情况时
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- 当你有什么想法想分享时
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- 当你觉得现在是个合适的聊天时机时
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{{
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"action": "proactive_reply",
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"reason": "你决定主动发言的具体原因",
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"topic": "你想说的内容主题（简洁描述）"
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}}
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动作：do_nothing
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动作描述：保持沉默，不主动发起对话。
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- 当你觉得现在不是合适的时机时
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- 当最近已经说得够多了时
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- 当对话氛围不适合插入时
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{{
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"action": "do_nothing",
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"reason": "决定保持沉默的原因"
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}}
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你必须从上面列出的可用action中选择一个。要像真人一样自然地思考和决策。
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请以严格的 JSON 格式输出，且仅包含 JSON 内容：
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""",
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"proactive_planner_prompt",
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)
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Prompt(
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"""
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动作：{action_name}
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动作描述：{action_description}
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{action_require}
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{{
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"action": "{action_name}",
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"target_message_id": "触发action的消息id",
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"reason": "触发action的原因"{action_parameters}
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}}
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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, chat_id: str, action_manager: ActionManager):
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self.chat_id = chat_id
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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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)
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self.last_obs_time_mark = 0.0
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async def _get_long_term_memory_context(self) -> str:
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"""
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获取长期记忆上下文
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"""
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try:
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# 1. 生成时间相关的关键词
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now = datetime.now()
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keywords = ["今天", "日程", "计划"]
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if 5 <= now.hour < 12:
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keywords.append("早上")
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elif 12 <= now.hour < 18:
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keywords.append("中午")
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else:
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keywords.append("晚上")
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# TODO: 添加与聊天对象相关的关键词
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# 2. 调用 hippocampus_manager 检索记忆
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retrieved_memories = await hippocampus_manager.get_memory_from_topic(
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valid_keywords=keywords, max_memory_num=5, max_memory_length=1
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)
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if not retrieved_memories:
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return "最近没有什么特别的记忆。"
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# 3. 格式化记忆
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memory_statements = []
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for topic, memory_item in retrieved_memories:
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memory_statements.append(f"关于'{topic}', 你记得'{memory_item}'。")
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return " ".join(memory_statements)
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except Exception as e:
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logger.error(f"获取长期记忆时出错: {e}")
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return "回忆时出现了一些问题。"
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async def _build_action_options(
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self,
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current_available_actions: Dict[str, ActionInfo],
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mode: ChatMode,
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target_prompt: str = "",
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) -> str:
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"""
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构建动作选项
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"""
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action_options_block = ""
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for action_name, action_info in current_available_actions.items():
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# TODO: 增加一个字段来判断action是否支持在PROACTIVE模式下使用
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param_text = ""
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if action_info.action_parameters:
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param_text = "\n" + "\n".join(
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f' "{p_name}":"{p_desc}"' for p_name, p_desc in action_info.action_parameters.items()
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)
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require_text = "\n".join(f"- {req}" for req in action_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=action_name,
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action_description=action_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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return action_options_block
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def find_message_by_id(self, message_id: str, message_id_list: list) -> Optional[Dict[str, Any]]:
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# sourcery skip: use-next
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"""
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根据message_id从message_id_list中查找对应的原始消息
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Args:
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message_id: 要查找的消息ID
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message_id_list: 消息ID列表，格式为[{'id': str, 'message': dict}, ...]
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Returns:
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找到的原始消息字典，如果未找到则返回None
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"""
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# 检测message_id 是否为纯数字
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if message_id.isdigit():
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message_id = f"m{message_id}"
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for item in message_id_list:
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if item.get("id") == message_id:
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return item.get("message")
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return None
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def get_latest_message(self, message_id_list: list) -> Optional[Dict[str, Any]]:
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"""
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获取消息列表中的最新消息
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Args:
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message_id_list: 消息ID列表，格式为[{'id': str, 'message': dict}, ...]
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Returns:
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最新的消息字典，如果列表为空则返回None
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"""
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if not message_id_list:
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return None
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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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async 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 not in ["no_action", "no_reply", "do_nothing"]:
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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", "no_reply", "reply", "do_nothing"] 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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# 如果是at_user动作且只有user_name，尝试转换为user_id
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if action == "at_user" and "user_name" in action_data and "user_id" not in action_data:
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user_name = action_data["user_name"]
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from src.person_info.person_info import get_person_info_manager
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user_info = await get_person_info_manager().get_person_info_by_name(user_name)
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if user_info and user_info.get("user_id"):
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action_data["user_id"] = user_info["user_id"]
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logger.info(f"成功将用户名 '{user_name}' 解析为 user_id '{user_info['user_id']}'")
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else:
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logger.warning(f"无法将用户名 '{user_name}' 解析为 user_id")
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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 plan(
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self,
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mode: ChatMode = ChatMode.FOCUS,
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loop_start_time: float = 0.0,
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available_actions: Optional[Dict[str, ActionInfo]] = None,
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pseudo_message: Optional[str] = None,
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) -> Tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]]:
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"""
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[注释] "大脑"规划器。
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统一决策是否进行聊天回复(reply)以及执行哪些actions。
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"""
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# --- 1. 准备上下文信息 ---
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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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# --- 2. 大脑统一决策 ---
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final_actions: List[Dict[str, Any]] = []
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try:
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prompt, used_message_id_list = await self.build_planner_prompt(
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is_group_chat=is_group_chat,
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chat_target_info=chat_target_info,
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current_available_actions=available_actions,
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mode=mode,
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)
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llm_content, _ = await self.planner_llm.generate_response_async(prompt=prompt)
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if llm_content:
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parsed_json = orjson.loads(repair_json(llm_content))
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# 确保处理的是列表
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if isinstance(parsed_json, dict):
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parsed_json = [parsed_json]
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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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final_actions.extend(await self._parse_single_action(item, used_message_id_list, list(available_actions.items())))
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# 如果是私聊且开启了强制回复，并且没有任何回复性action，则强制添加reply
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if not is_group_chat and global_config.chat.force_reply_private:
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has_reply_action = any(a.get("action_type") == "reply" for a in final_actions)
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if not has_reply_action:
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final_actions.append({
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||
"action_type": "reply",
|
||
"reasoning": "私聊强制回复",
|
||
"action_data": {},
|
||
"action_message": self.get_latest_message(used_message_id_list),
|
||
"available_actions": available_actions,
|
||
})
|
||
logger.info(f"{self.log_prefix}私聊强制回复已触发，添加 'reply' 动作")
|
||
|
||
logger.info(f"{self.log_prefix}大脑决策: {[a.get('action_type') for a in final_actions]}")
|
||
|
||
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()}")
|
||
final_actions.append({"action_type": "no_action", "reasoning": f"大脑处理错误: {e}"})
|
||
|
||
# --- 3. 后处理 ---
|
||
final_actions = self._filter_no_actions(final_actions)
|
||
|
||
# === 概率模式后处理：根据配置决定是否强制添加 emoji 动作 ===
|
||
if global_config.emoji.emoji_activate_type == 'random':
|
||
has_reply_action = any(a.get("action_type") == "reply" for a in final_actions)
|
||
if has_reply_action:
|
||
# 检查此动作是否已被选择
|
||
is_already_chosen = any(a.get("action_type") == 'emoji' for a in final_actions)
|
||
if not is_already_chosen:
|
||
if random.random() < global_config.emoji.emoji_chance:
|
||
logger.info(f"{self.log_prefix}根据概率 '{global_config.emoji.emoji_chance}' 添加 emoji 动作")
|
||
final_actions.append({
|
||
"action_type": 'emoji',
|
||
"reasoning": f"根据概率 {global_config.emoji.emoji_chance} 自动添加",
|
||
"action_data": {},
|
||
"action_message": self.get_latest_message(used_message_id_list),
|
||
"available_actions": available_actions,
|
||
})
|
||
|
||
if not final_actions:
|
||
final_actions = [
|
||
{
|
||
"action_type": "no_action",
|
||
"reasoning": "规划器选择不执行动作",
|
||
"action_data": {}, "action_message": None, "available_actions": available_actions
|
||
}
|
||
]
|
||
|
||
final_target_message = next((act.get("action_message") for act in final_actions if act.get("action_message")), None)
|
||
|
||
# 记录每个动作的原因
|
||
for action_info in final_actions:
|
||
action_type = action_info.get("action_type", "N/A")
|
||
reasoning = action_info.get("reasoning", "无")
|
||
logger.info(f"{self.log_prefix}决策: [{action_type}]，原因: {reasoning}")
|
||
|
||
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,
|
||
chat_target_info: Optional[dict],
|
||
current_available_actions: Dict[str, ActionInfo],
|
||
mode: ChatMode = ChatMode.FOCUS,
|
||
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 ---
|
||
time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
|
||
bot_name = global_config.bot.nickname
|
||
bot_nickname = (
|
||
f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
|
||
)
|
||
bot_core_personality = global_config.personality.personality_core
|
||
identity_block = f"你的名字是{bot_name}{bot_nickname}，你{bot_core_personality}："
|
||
|
||
schedule_block = ""
|
||
if global_config.planning_system.schedule_enable:
|
||
if current_activity := schedule_manager.get_current_activity():
|
||
schedule_block = f"你当前正在：{current_activity},但注意它与群聊的聊天无关。"
|
||
|
||
mood_block = ""
|
||
if global_config.mood.enable_mood:
|
||
chat_mood = mood_manager.get_mood_by_chat_id(self.chat_id)
|
||
mood_block = f"你现在的心情是：{chat_mood.mood_state}"
|
||
|
||
# --- 根据模式构建不同的Prompt ---
|
||
if mode == ChatMode.PROACTIVE:
|
||
long_term_memory_block = await self._get_long_term_memory_context()
|
||
|
||
# 获取最近的聊天记录用于主动思考决策
|
||
message_list_short = get_raw_msg_before_timestamp_with_chat(
|
||
chat_id=self.chat_id,
|
||
timestamp=time.time(),
|
||
limit=int(global_config.chat.max_context_size * 0.2), # 主动思考时只看少量最近消息
|
||
)
|
||
chat_content_block, message_id_list = build_readable_messages_with_id(
|
||
messages=message_list_short,
|
||
timestamp_mode="normal",
|
||
truncate=False,
|
||
show_actions=False,
|
||
)
|
||
|
||
prompt_template = await global_prompt_manager.get_prompt_async("proactive_planner_prompt")
|
||
actions_before_now = get_actions_by_timestamp_with_chat(
|
||
chat_id=self.chat_id,
|
||
timestamp_start=time.time() - 3600,
|
||
timestamp_end=time.time(),
|
||
limit=5,
|
||
)
|
||
actions_before_now_block = build_readable_actions(actions=actions_before_now)
|
||
actions_before_now_block = f"你刚刚选择并执行过的action是：\n{actions_before_now_block}"
|
||
|
||
prompt = prompt_template.format(
|
||
time_block=time_block,
|
||
identity_block=identity_block,
|
||
schedule_block=schedule_block,
|
||
mood_block=mood_block,
|
||
long_term_memory_block=long_term_memory_block,
|
||
chat_content_block=chat_content_block or "最近没有聊天内容。",
|
||
actions_before_now_block=actions_before_now_block,
|
||
)
|
||
return prompt, message_id_list
|
||
|
||
# --- FOCUS 和 NORMAL 模式的逻辑 ---
|
||
message_list_before_now = get_raw_msg_before_timestamp_with_chat(
|
||
chat_id=self.chat_id,
|
||
timestamp=time.time(),
|
||
limit=int(global_config.chat.max_context_size * 0.6),
|
||
)
|
||
chat_content_block, message_id_list = build_readable_messages_with_id(
|
||
messages=message_list_before_now,
|
||
timestamp_mode="normal",
|
||
read_mark=self.last_obs_time_mark,
|
||
truncate=True,
|
||
show_actions=True,
|
||
)
|
||
|
||
actions_before_now = get_actions_by_timestamp_with_chat(
|
||
chat_id=self.chat_id,
|
||
timestamp_start=time.time() - 3600,
|
||
timestamp_end=time.time(),
|
||
limit=5,
|
||
)
|
||
|
||
actions_before_now_block = build_readable_actions(actions=actions_before_now)
|
||
actions_before_now_block = f"你刚刚选择并执行过的action是：\n{actions_before_now_block}"
|
||
|
||
if refresh_time:
|
||
self.last_obs_time_mark = time.time()
|
||
|
||
mentioned_bonus = ""
|
||
if global_config.chat.mentioned_bot_inevitable_reply:
|
||
mentioned_bonus = "\n- 有人提到你"
|
||
if global_config.chat.at_bot_inevitable_reply:
|
||
mentioned_bonus = "\n- 有人提到你，或者at你"
|
||
|
||
if mode == ChatMode.FOCUS:
|
||
no_action_block = """
|
||
动作：no_action
|
||
动作描述：不选择任何动作
|
||
{{
|
||
"action": "no_action",
|
||
"reason":"不动作的原因"
|
||
}}
|
||
|
||
动作：no_reply
|
||
动作描述：不进行回复，等待合适的回复时机
|
||
- 当你刚刚发送了消息，没有人回复时，选择no_reply
|
||
- 当你一次发送了太多消息，为了避免打扰聊天节奏，选择no_reply
|
||
{{
|
||
"action": "no_reply",
|
||
"reason":"不回复的原因"
|
||
}}
|
||
"""
|
||
else: # NORMAL Mode
|
||
no_action_block = """重要说明：
|
||
- 'reply' 表示只进行普通聊天回复，不执行任何额外动作
|
||
- 其他action表示在普通回复的基础上，执行相应的额外动作
|
||
{{
|
||
"action": "reply",
|
||
"target_message_id":"触发action的消息id",
|
||
"reason":"回复的原因"
|
||
}}"""
|
||
|
||
chat_context_description = "你现在正在一个群聊中"
|
||
chat_target_name = None
|
||
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"
|
||
|
||
action_options_block = await self._build_action_options(current_available_actions, mode)
|
||
|
||
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。"
|
||
|
||
custom_prompt_block = ""
|
||
if global_config.custom_prompt.planner_custom_prompt_content:
|
||
custom_prompt_block = global_config.custom_prompt.planner_custom_prompt_content
|
||
|
||
from src.person_info.person_info import get_person_info_manager
|
||
users_in_chat_str = ""
|
||
if is_group_chat and chat_target_info and chat_target_info.get("group_id"):
|
||
user_list = await get_person_info_manager().get_specific_value_list("person_name", lambda x: x is not None)
|
||
if user_list:
|
||
users_in_chat_str = "当前聊天中的用户列表（用于@）：\n" + "\n".join([f"- {name} (ID: {pid})" for pid, name in user_list.items()]) + "\n"
|
||
|
||
|
||
planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
|
||
prompt = planner_prompt_template.format(
|
||
schedule_block=schedule_block,
|
||
mood_block=mood_block,
|
||
time_block=time_block,
|
||
chat_context_description=chat_context_description,
|
||
chat_content_block=chat_content_block,
|
||
actions_before_now_block=actions_before_now_block,
|
||
mentioned_bonus=mentioned_bonus,
|
||
no_action_block=no_action_block,
|
||
mentioned_bonus=mentioned_bonus,
|
||
action_options_text=action_options_block,
|
||
moderation_prompt=moderation_prompt_block,
|
||
identity_block=identity_block,
|
||
custom_prompt_block=custom_prompt_block,
|
||
bot_name=bot_name,
|
||
users_in_chat=users_in_chat_str
|
||
)
|
||
return prompt, message_id_list
|
||
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", []
|
||
|
||
def get_necessary_info(self) -> Tuple[bool, Optional[dict], Dict[str, ActionInfo]]:
|
||
"""
|
||
获取 Planner 需要的必要信息
|
||
"""
|
||
is_group_chat = True
|
||
is_group_chat, chat_target_info = get_chat_type_and_target_info(self.chat_id)
|
||
logger.debug(f"{self.log_prefix}获取到聊天信息 - 群聊: {is_group_chat}, 目标信息: {chat_target_info}")
|
||
|
||
current_available_actions_dict = self.action_manager.get_using_actions()
|
||
|
||
# 获取完整的动作信息
|
||
all_registered_actions: Dict[str, ActionInfo] = component_registry.get_components_by_type( # type: ignore
|
||
ComponentType.ACTION
|
||
)
|
||
current_available_actions = {}
|
||
for action_name in current_available_actions_dict:
|
||
if action_name in all_registered_actions:
|
||
current_available_actions[action_name] = all_registered_actions[action_name]
|
||
else:
|
||
logger.warning(f"{self.log_prefix}使用中的动作 {action_name} 未在已注册动作中找到")
|
||
|
||
# 将no_reply作为系统级特殊动作添加到可用动作中
|
||
# no_reply虽然是系统级决策，但需要让规划器认为它是可用的
|
||
no_reply_info = ActionInfo(
|
||
name="no_reply",
|
||
component_type=ComponentType.ACTION,
|
||
description="系统级动作：选择不回复消息的决策",
|
||
action_parameters={},
|
||
activation_keywords=[],
|
||
plugin_name="SYSTEM",
|
||
enabled=True, # 始终启用
|
||
parallel_action=False,
|
||
)
|
||
current_available_actions["no_reply"] = no_reply_info
|
||
|
||
return is_group_chat, chat_target_info, current_available_actions
|
||
|
||
|
||
init_prompt()
|