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Merge pull request #627 from lmst2/refactor

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添加有关时区的设置，可以在bot_config里设置时区，来改变机器人作息，以及一些llm logger的小tweak
This commit is contained in:
SengokuCola
2025-04-04 14:15:12 +08:00
committed by GitHub
parent 0a1c2cccb0 e89f8d9844
commit 00b523acc3
6 changed files with 51 additions and 35 deletions
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4
.gitignore vendored
View File

@@ -6,6 +6,10 @@ log/
logs/
/test
/src/test
nonebot-maibot-adapter/
*.zip
run.bat
run.py
message_queue_content.txt
message_queue_content.bat
message_queue_window.bat

7
src/plugins/config/config.py
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@@ -1,6 +1,7 @@
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional
from dateutil import tz
import tomli
import tomlkit
@@ -151,6 +152,7 @@ class BotConfig:
PROMPT_SCHEDULE_GEN = "无日程"
SCHEDULE_DOING_UPDATE_INTERVAL: int = 300 # 日程表更新间隔 单位秒
SCHEDULE_TEMPERATURE: float = 0.5 # 日程表温度，建议0.5-1.0
TIME_ZONE: str = "Asia/Shanghai" # 时区
# message
MAX_CONTEXT_SIZE: int = 15 # 上下文最大消息数
@@ -355,6 +357,11 @@ class BotConfig:
)
if config.INNER_VERSION in SpecifierSet(">=1.0.2"):
config.SCHEDULE_TEMPERATURE = schedule_config.get("schedule_temperature", config.SCHEDULE_TEMPERATURE)
time_zone = schedule_config.get("time_zone", config.TIME_ZONE)
if tz.gettz(time_zone) is None:
logger.error(f"无效的时区: {time_zone}，使用默认值: {config.TIME_ZONE}")
else:
config.TIME_ZONE = time_zone
def emoji(parent: dict):
emoji_config = parent["emoji"]

1
src/plugins/memory_system/Hippocampus.py
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@@ -14,7 +14,6 @@ from src.common.logger import get_module_logger, LogConfig, MEMORY_STYLE_CONFIG
from src.plugins.memory_system.sample_distribution import MemoryBuildScheduler # 分布生成器
from .memory_config import MemoryConfig
def get_closest_chat_from_db(length: int, timestamp: str):
# print(f"获取最接近指定时间戳的聊天记录，长度: {length}, 时间戳: {timestamp}")
# print(f"当前时间: {timestamp},转换后时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(timestamp))}")

56
src/plugins/models/utils_model.py
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@@ -153,7 +153,7 @@ class LLM_request:
# 合并重试策略
default_retry = {
"max_retries": 3,
"max_retries": 10,
"base_wait": 15,
"retry_codes": [429, 413, 500, 503],
"abort_codes": [400, 401, 402, 403],
@@ -179,9 +179,6 @@ class LLM_request:
# logger.debug(f"{logger_msg}发送请求到URL: {api_url}")
# logger.info(f"使用模型: {self.model_name}")
# 流式输出标志
if stream_mode:
payload["stream"] = stream_mode
# 构建请求体
if image_base64:
@@ -189,6 +186,11 @@ class LLM_request:
elif payload is None:
payload = await self._build_payload(prompt)
# 流式输出标志
# 先构建payload，再添加流式输出标志
if stream_mode:
payload["stream"] = stream_mode
for retry in range(policy["max_retries"]):
try:
# 使用上下文管理器处理会话
@@ -203,21 +205,21 @@ class LLM_request:
# 处理需要重试的状态码
if response.status in policy["retry_codes"]:
wait_time = policy["base_wait"] * (2**retry)
logger.warning(f"错误码: {response.status}, 等待 {wait_time}秒后重试")
logger.warning(f"模型 {self.model_name} 错误码: {response.status}, 等待 {wait_time}秒后重试")
if response.status == 413:
logger.warning("请求体过大，尝试压缩...")
image_base64 = compress_base64_image_by_scale(image_base64)
payload = await self._build_payload(prompt, image_base64, image_format)
elif response.status in [500, 503]:
logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}")
logger.error(f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}")
raise RuntimeError("服务器负载过高，模型恢复失败QAQ")
else:
logger.warning(f"请求限制(429)，等待{wait_time}秒后重试...")
logger.warning(f"模型 {self.model_name} 请求限制(429)，等待{wait_time}秒后重试...")
await asyncio.sleep(wait_time)
continue
elif response.status in policy["abort_codes"]:
logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}")
logger.error(f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}")
# 尝试获取并记录服务器返回的详细错误信息
try:
error_json = await response.json()
@@ -319,9 +321,9 @@ class LLM_request:
flag_delta_content_finished = True
except Exception as e:
logger.exception(f"解析流式输出错误: {str(e)}")
logger.exception(f"模型 {self.model_name} 解析流式输出错误: {str(e)}")
except GeneratorExit:
logger.warning("流式输出被中断，正在清理资源...")
logger.warning("模型 {self.model_name} 流式输出被中断，正在清理资源...")
# 确保资源被正确清理
await response.release()
# 返回已经累积的内容
@@ -335,7 +337,7 @@ class LLM_request:
else self._default_response_handler(result, user_id, request_type, endpoint)
)
except Exception as e:
logger.error(f"处理流式输出时发生错误: {str(e)}")
logger.error(f"模型 {self.model_name} 处理流式输出时发生错误: {str(e)}")
# 确保在发生错误时也能正确清理资源
try:
await response.release()
@@ -378,21 +380,21 @@ class LLM_request:
except (aiohttp.ClientError, asyncio.TimeoutError) as e:
if retry < policy["max_retries"] - 1:
wait_time = policy["base_wait"] * (2**retry)
logger.error(f"网络错误，等待{wait_time}秒后重试... 错误: {str(e)}")
logger.error(f"模型 {self.model_name} 网络错误，等待{wait_time}秒后重试... 错误: {str(e)}")
await asyncio.sleep(wait_time)
continue
else:
logger.critical(f"网络错误达到最大重试次数: {str(e)}")
logger.critical(f"模型 {self.model_name} 网络错误达到最大重试次数: {str(e)}")
raise RuntimeError(f"网络请求失败: {str(e)}") from e
except Exception as e:
logger.critical(f"未预期的错误: {str(e)}")
logger.critical(f"模型 {self.model_name} 未预期的错误: {str(e)}")
raise RuntimeError(f"请求过程中发生错误: {str(e)}") from e
except aiohttp.ClientResponseError as e:
# 处理aiohttp抛出的响应错误
if retry < policy["max_retries"] - 1:
wait_time = policy["base_wait"] * (2**retry)
logger.error(f"HTTP响应错误，等待{wait_time}秒后重试... 状态码: {e.status}, 错误: {e.message}")
logger.error(f"模型 {self.model_name} HTTP响应错误，等待{wait_time}秒后重试... 状态码: {e.status}, 错误: {e.message}")
try:
if hasattr(e, "response") and e.response and hasattr(e.response, "text"):
error_text = await e.response.text()
@@ -403,27 +405,27 @@ class LLM_request:
if "error" in error_item and isinstance(error_item["error"], dict):
error_obj = error_item["error"]
logger.error(
f"服务器错误详情: 代码={error_obj.get('code')}, "
f"模型 {self.model_name} 服务器错误详情: 代码={error_obj.get('code')}, "
f"状态={error_obj.get('status')}, "
f"消息={error_obj.get('message')}"
)
elif isinstance(error_json, dict) and "error" in error_json:
error_obj = error_json.get("error", {})
logger.error(
f"服务器错误详情: 代码={error_obj.get('code')}, "
f"模型 {self.model_name} 服务器错误详情: 代码={error_obj.get('code')}, "
f"状态={error_obj.get('status')}, "
f"消息={error_obj.get('message')}"
)
else:
logger.error(f"服务器错误响应: {error_json}")
logger.error(f"模型 {self.model_name} 服务器错误响应: {error_json}")
except (json.JSONDecodeError, TypeError) as json_err:
logger.warning(f"响应不是有效的JSON: {str(json_err)}, 原始内容: {error_text[:200]}")
logger.warning(f"模型 {self.model_name} 响应不是有效的JSON: {str(json_err)}, 原始内容: {error_text[:200]}")
except (AttributeError, TypeError, ValueError) as parse_err:
logger.warning(f"无法解析响应错误内容: {str(parse_err)}")
logger.warning(f"模型 {self.model_name} 无法解析响应错误内容: {str(parse_err)}")
await asyncio.sleep(wait_time)
else:
logger.critical(f"HTTP响应错误达到最大重试次数: 状态码: {e.status}, 错误: {e.message}")
logger.critical(f"模型 {self.model_name} HTTP响应错误达到最大重试次数: 状态码: {e.status}, 错误: {e.message}")
# 安全地检查和记录请求详情
if (
image_base64
@@ -440,14 +442,14 @@ class LLM_request:
f"{image_base64[:10]}...{image_base64[-10:]}"
)
logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {payload}")
raise RuntimeError(f"API请求失败: 状态码 {e.status}, {e.message}") from e
raise RuntimeError(f"模型 {self.model_name} API请求失败: 状态码 {e.status}, {e.message}") from e
except Exception as e:
if retry < policy["max_retries"] - 1:
wait_time = policy["base_wait"] * (2**retry)
logger.error(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
logger.error(f"模型 {self.model_name} 请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
await asyncio.sleep(wait_time)
else:
logger.critical(f"请求失败: {str(e)}")
logger.critical(f"模型 {self.model_name} 请求失败: {str(e)}")
# 安全地检查和记录请求详情
if (
image_base64
@@ -464,10 +466,10 @@ class LLM_request:
f"{image_base64[:10]}...{image_base64[-10:]}"
)
logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {payload}")
raise RuntimeError(f"API请求失败: {str(e)}") from e
raise RuntimeError(f"模型 {self.model_name} API请求失败: {str(e)}") from e
logger.error("达到最大重试次数，请求仍然失败")
raise RuntimeError("达到最大重试次数，API请求仍然失败")
logger.error(f"模型 {self.model_name} 达到最大重试次数，请求仍然失败，错误: {str(e)}")
raise RuntimeError(f"模型 {self.model_name} 达到最大重试次数，API请求仍然失败，错误: {str(e)}")
async def _transform_parameters(self, params: dict) -> dict:
"""

17
src/plugins/schedule/schedule_generator.py
View File

@@ -3,6 +3,7 @@ import os
import sys
from typing import Dict
import asyncio
from dateutil import tz
# 添加项目根目录到 Python 路径
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
@@ -13,6 +14,8 @@ from src.common.logger import get_module_logger, SCHEDULE_STYLE_CONFIG, LogConfi
from src.plugins.models.utils_model import LLM_request # noqa: E402
from src.plugins.config.config import global_config # noqa: E402
TIME_ZONE = tz.gettz(global_config.TIME_ZONE) # 设置时区
schedule_config = LogConfig(
# 使用海马体专用样式
@@ -44,7 +47,7 @@ class ScheduleGenerator:
self.personality = ""
self.behavior = ""
self.start_time = datetime.datetime.now()
self.start_time = datetime.datetime.now(TIME_ZONE)
self.schedule_doing_update_interval = 300 # 最好大于60
@@ -74,7 +77,7 @@ class ScheduleGenerator:
while True:
# print(self.get_current_num_task(1, True))
current_time = datetime.datetime.now()
current_time = datetime.datetime.now(TIME_ZONE)
# 检查是否需要重新生成日程（日期变化）
if current_time.date() != self.start_time.date():
@@ -100,7 +103,7 @@ class ScheduleGenerator:
Returns:
tuple: (today_schedule_text, today_schedule) 今天的日程文本和解析后的日程字典
"""
today = datetime.datetime.now()
today = datetime.datetime.now(TIME_ZONE)
yesterday = today - datetime.timedelta(days=1)
# 先检查昨天的日程
@@ -156,7 +159,7 @@ class ScheduleGenerator:
"""打印完整的日程安排"""
if not self.today_schedule_text:
logger.warning("今日日程有误，将在下次运行时重新生成")
db.schedule.delete_one({"date": datetime.datetime.now().strftime("%Y-%m-%d")})
db.schedule.delete_one({"date": datetime.datetime.now(TIME_ZONE).strftime("%Y-%m-%d")})
else:
logger.info("=== 今日日程安排 ===")
logger.info(self.today_schedule_text)
@@ -165,7 +168,7 @@ class ScheduleGenerator:
async def update_today_done_list(self):
# 更新数据库中的 today_done_list
today_str = datetime.datetime.now().strftime("%Y-%m-%d")
today_str = datetime.datetime.now(TIME_ZONE).strftime("%Y-%m-%d")
existing_schedule = db.schedule.find_one({"date": today_str})
if existing_schedule:
@@ -177,7 +180,7 @@ class ScheduleGenerator:
async def move_doing(self, mind_thinking: str = ""):
try:
current_time = datetime.datetime.now()
current_time = datetime.datetime.now(TIME_ZONE)
if mind_thinking:
doing_prompt = self.construct_doing_prompt(current_time, mind_thinking)
else:
@@ -246,7 +249,7 @@ class ScheduleGenerator:
def save_today_schedule_to_db(self):
"""保存日程到数据库，同时初始化 today_done_list"""
date_str = datetime.datetime.now().strftime("%Y-%m-%d")
date_str = datetime.datetime.now(TIME_ZONE).strftime("%Y-%m-%d")
schedule_data = {
"date": date_str,
"schedule": self.today_schedule_text,

1
template/bot_config_template.toml
View File

@@ -48,6 +48,7 @@ enable_schedule_gen = true # 是否启用日程表(尚未完成)
prompt_schedule_gen = "用几句话描述描述性格特点或行动规律，这个特征会用来生成日程表"
schedule_doing_update_interval = 900 # 日程表更新间隔 单位秒
schedule_temperature = 0.3 # 日程表温度，建议0.3-0.6
time_zone = "Asia/Shanghai" # 给你的机器人设置时区，可以解决运行电脑时区和国内时区不同的情况，或者模拟国外留学生日程
[platforms] # 必填项目，填写每个平台适配器提供的链接
nonebot-qq="http://127.0.0.1:18002/api/message"