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style: 格式化代码

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This commit is contained in:
John Richard
2025-10-02 19:38:39 +08:00
parent d5627b0661
commit ecb02cae31
111 changed files with 2344 additions and 2316 deletions
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12
src/plugins/built_in/proactive_thinker/plugin.py
View File

@@ -1,27 +1,23 @@
from typing import List, Tuple, Union, Type, Optional
from typing import List, Tuple, Type
from src.common.logger import get_logger
from src.config.official_configs import AffinityFlowConfig
from src.plugin_system.base.base_plugin import BasePlugin
from src.plugin_system import (
BasePlugin,
ConfigField,
register_plugin,
plugin_manage_api,
component_manage_api,
ComponentInfo,
ComponentType,
EventHandlerInfo,
EventType,
BaseEventHandler,
)
from .proacive_thinker_event import ProactiveThinkerEventHandler
logger = get_logger(__name__)
@register_plugin
class ProactiveThinkerPlugin(BasePlugin):
"""一个主动思考的插件，但现在还只是个空壳子"""
plugin_name: str = "proactive_thinker"
enable_plugin: bool = False
dependencies: list[str] = []
@@ -33,6 +29,7 @@ class ProactiveThinkerPlugin(BasePlugin):
"config_version": ConfigField(type=str, default="1.1.0", description="配置文件版本"),
},
}
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@@ -42,4 +39,3 @@ class ProactiveThinkerPlugin(BasePlugin):
(ProactiveThinkerEventHandler.get_handler_info(), ProactiveThinkerEventHandler)
]
return components

12
src/plugins/built_in/proactive_thinker/proacive_thinker_event.py
View File

@@ -2,7 +2,7 @@ import asyncio
import random
import time
from datetime import datetime
from typing import List, Union, Type, Optional
from typing import List, Union
from maim_message import UserInfo
@@ -69,7 +69,7 @@ class ColdStartTask(AsyncTask):
# 创建 UserInfo 对象，这是创建聊天流的必要信息
user_info = UserInfo(platform=platform, user_id=str(user_id), user_nickname=user_nickname)
# 【关键步骤】主动创建聊天流。
# 创建后，该用户就进入了机器人的“好友列表”，后续将由 ProactiveThinkingTask 接管
stream = await self.chat_manager.get_or_create_stream(platform, user_info)
@@ -175,10 +175,12 @@ class ProactiveThinkingTask(AsyncTask):
# 2. 【核心逻辑】检查聊天冷却时间是否足够长
time_since_last_active = time.time() - stream.last_active_time
if time_since_last_active > next_interval:
logger.info(f"【日常唤醒】聊天流 {stream.stream_id} 已冷却 {time_since_last_active:.2f} 秒，触发主动对话。")
logger.info(
f"【日常唤醒】聊天流 {stream.stream_id} 已冷却 {time_since_last_active:.2f} 秒，触发主动对话。"
)
await self.executor.execute(stream_id=stream.stream_id, start_mode="wake_up")
# 【关键步骤】在触发后，立刻更新活跃时间并保存。
# 这可以防止在同一个检查周期内，对同一个目标因为意外的延迟而发送多条消息。
stream.update_active_time()

138
src/plugins/built_in/proactive_thinker/proactive_thinker_executor.py
View File

@@ -3,7 +3,16 @@ from typing import Optional, Dict, Any
from datetime import datetime
from src.common.logger import get_logger
from src.plugin_system.apis import chat_api, person_api, schedule_api, send_api, llm_api, message_api, generator_api, database_api
from src.plugin_system.apis import (
chat_api,
person_api,
schedule_api,
send_api,
llm_api,
message_api,
generator_api,
database_api,
)
from src.config.config import global_config, model_config
from src.person_info.person_info import get_person_info_manager
@@ -39,17 +48,16 @@ class ProactiveThinkerExecutor:
# 2. 决策阶段
decision_result = await self._make_decision(context, start_mode)
if not decision_result or not decision_result.get("should_reply"):
reason = decision_result.get("reason", "未提供") if decision_result else "决策过程返回None"
logger.info(f"决策结果为：不回复。原因: {reason}")
await database_api.store_action_info(
chat_stream=self._get_stream_from_id(stream_id),
action_name="proactive_decision",
action_prompt_display=f"主动思考决定不回复,原因: {reason}",
action_done = True,
action_data=decision_result
)
chat_stream=self._get_stream_from_id(stream_id),
action_name="proactive_decision",
action_prompt_display=f"主动思考决定不回复,原因: {reason}",
action_done=True,
action_data=decision_result,
)
return
# 3. 规划与执行阶段
@@ -59,15 +67,17 @@ class ProactiveThinkerExecutor:
chat_stream=self._get_stream_from_id(stream_id),
action_name="proactive_decision",
action_prompt_display=f"主动思考决定回复,原因: {reason},话题:{topic}",
action_done = True,
action_data=decision_result
action_done=True,
action_data=decision_result,
)
logger.info(f"决策结果为：回复。话题: {topic}")
plan_prompt = self._build_plan_prompt(context, start_mode, topic, reason)
is_success, response, _, _ = await llm_api.generate_with_model(prompt=plan_prompt, model_config=model_config.model_task_config.utils)
is_success, response, _, _ = await llm_api.generate_with_model(
prompt=plan_prompt, model_config=model_config.model_task_config.utils
)
if is_success and response:
stream = self._get_stream_from_id(stream_id)
if stream:
@@ -104,33 +114,41 @@ class ProactiveThinkerExecutor:
if not user_info or not user_info.platform or not user_info.user_id:
logger.warning(f"Stream {stream_id} 的 user_info 不完整")
return None
person_id = person_api.get_person_id(user_info.platform, int(user_info.user_id))
person_info_manager = get_person_info_manager()
# 获取日程
schedules = await schedule_api.ScheduleAPI.get_today_schedule()
schedule_context = "\n".join([f"- {s['title']} ({s['start_time']}-{s['end_time']})" for s in schedules]) if schedules else "今天没有日程安排。"
schedule_context = (
"\n".join([f"- {s['title']} ({s['start_time']}-{s['end_time']})" for s in schedules])
if schedules
else "今天没有日程安排。"
)
# 获取关系信息
short_impression = await person_info_manager.get_value(person_id, "short_impression") or "无"
impression = await person_info_manager.get_value(person_id, "impression") or "无"
attitude = await person_info_manager.get_value(person_id, "attitude") or 50
# 获取最近聊天记录
recent_messages = await message_api.get_recent_messages(stream_id, limit=10)
recent_chat_history = await message_api.build_readable_messages_to_str(recent_messages) if recent_messages else "无"
recent_chat_history = (
await message_api.build_readable_messages_to_str(recent_messages) if recent_messages else "无"
)
# 获取最近的动作历史
action_history = await database_api.db_query(
database_api.MODEL_MAPPING["ActionRecords"],
filters={"chat_id": stream_id, "action_name": "proactive_decision"},
limit=3,
order_by=["-time"]
order_by=["-time"],
)
action_history_context = "无"
if isinstance(action_history, list):
action_history_context = "\n".join([f"- {a['action_data']}" for a in action_history if isinstance(a, dict)]) or "无"
action_history_context = (
"\n".join([f"- {a['action_data']}" for a in action_history if isinstance(a, dict)]) or "无"
)
return {
"person_id": person_id,
@@ -138,47 +156,43 @@ class ProactiveThinkerExecutor:
"schedule_context": schedule_context,
"recent_chat_history": recent_chat_history,
"action_history_context": action_history_context,
"relationship": {
"short_impression": short_impression,
"impression": impression,
"attitude": attitude
},
"relationship": {"short_impression": short_impression, "impression": impression, "attitude": attitude},
"persona": {
"core": global_config.personality.personality_core,
"side": global_config.personality.personality_side,
"identity": global_config.personality.identity,
},
"current_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
"current_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
}
async def _make_decision(self, context: Dict[str, Any], start_mode: str) -> Optional[Dict[str, Any]]:
"""
决策模块：判断是否应该主动发起对话，以及聊什么话题
"""
persona = context['persona']
user_info = context['user_info']
relationship = context['relationship']
persona = context["persona"]
user_info = context["user_info"]
relationship = context["relationship"]
prompt = f"""
# 角色
你的名字是{global_config.bot.nickname}，你的人设如下：
- 核心人设: {persona['core']}
- 侧面人设: {persona['side']}
- 身份: {persona['identity']}
- 核心人设: {persona["core"]}
- 侧面人设: {persona["side"]}
- 身份: {persona["identity"]}
# 任务
现在是 {context['current_time']}，你需要根据当前的情境，决定是否要主动向用户 '{user_info.user_nickname}' 发起对话。
现在是 {context["current_time"]}，你需要根据当前的情境，决定是否要主动向用户 '{user_info.user_nickname}' 发起对话。
# 情境分析
1. **启动模式**: {start_mode} ({'初次见面/很久未见' if start_mode == 'cold_start' else '日常唤醒'})
1. **启动模式**: {start_mode} ({"初次见面/很久未见" if start_mode == "cold_start" else "日常唤醒"})
2. **你的日程**:
{context['schedule_context']}
{context["schedule_context"]}
3. **你和Ta的关系**:
- 简短印象: {relationship['short_impression']}
- 详细印象: {relationship['impression']}
- 好感度: {relationship['attitude']}/100
- 简短印象: {relationship["short_impression"]}
- 详细印象: {relationship["impression"]}
- 好感度: {relationship["attitude"]}/100
4. **最近的聊天摘要**:
{context['recent_chat_history']}
{context["recent_chat_history"]}
# 决策指令
请综合以上所有信息，做出决策。你的决策需要以JSON格式输出，包含以下字段：
@@ -204,9 +218,11 @@ class ProactiveThinkerExecutor:
请输出你的决策:
"""
is_success, response, _, _ = await llm_api.generate_with_model(prompt=prompt, model_config=model_config.model_task_config.utils)
is_success, response, _, _ = await llm_api.generate_with_model(
prompt=prompt, model_config=model_config.model_task_config.utils
)
if not is_success:
return {"should_reply": False, "reason": "决策模型生成失败"}
@@ -222,17 +238,17 @@ class ProactiveThinkerExecutor:
"""
根据启动模式和决策话题，构建最终的规划提示词
"""
persona = context['persona']
user_info = context['user_info']
relationship = context['relationship']
persona = context["persona"]
user_info = context["user_info"]
relationship = context["relationship"]
if start_mode == "cold_start":
prompt = f"""
# 角色
你的名字是{global_config.bot.nickname}，你的人设如下：
- 核心人设: {persona['core']}
- 侧面人设: {persona['side']}
- 身份: {persona['identity']}
- 核心人设: {persona["core"]}
- 侧面人设: {persona["side"]}
- 身份: {persona["identity"]}
# 任务
你需要主动向一个新朋友 '{user_info.user_nickname}' 发起对话。这是你们的第一次交流，或者很久没聊了。
@@ -240,9 +256,9 @@ class ProactiveThinkerExecutor:
# 决策上下文
- **决策理由**: {reason}
- **你和Ta的关系**:
- 简短印象: {relationship['short_impression']}
- 详细印象: {relationship['impression']}
- 好感度: {relationship['attitude']}/100
- 简短印象: {relationship["short_impression"]}
- 详细印象: {relationship["impression"]}
- 好感度: {relationship["attitude"]}/100
# 对话指引
- 你的目标是“破冰”，让对话自然地开始。
@@ -254,26 +270,26 @@ class ProactiveThinkerExecutor:
prompt = f"""
# 角色
你的名字是{global_config.bot.nickname}，你的人设如下：
- 核心人设: {persona['core']}
- 侧面人设: {persona['side']}
- 身份: {persona['identity']}
- 核心人设: {persona["core"]}
- 侧面人设: {persona["side"]}
- 身份: {persona["identity"]}
# 任务
现在是 {context['current_time']}，你需要主动向你的朋友 '{user_info.user_nickname}' 发起对话。
现在是 {context["current_time"]}，你需要主动向你的朋友 '{user_info.user_nickname}' 发起对话。
# 决策上下文
- **决策理由**: {reason}
# 情境分析
1. **你的日程**:
{context['schedule_context']}
{context["schedule_context"]}
2. **你和Ta的关系**:
- 详细印象: {relationship['impression']}
- 好感度: {relationship['attitude']}/100
- 详细印象: {relationship["impression"]}
- 好感度: {relationship["attitude"]}/100
3. **最近的聊天摘要**:
{context['recent_chat_history']}
{context["recent_chat_history"]}
4. **你最近的相关动作**:
{context['action_history_context']}
{context["action_history_context"]}
# 对话指引
- 你决定和Ta聊聊关于“{topic}”的话题。