🤖 自动格式化代码 [skip ci]

This commit is contained in:
github-actions[bot]
2025-06-02 13:15:04 +00:00
parent 20d68a7896
commit 243d5184e6
8 changed files with 30 additions and 34 deletions

View File

@@ -23,11 +23,7 @@ class EmojiAction(BaseAction):
action_parameters: dict[str:str] = {
"description": "文字描述你想要发送的表情",
}
action_require: list[str] = [
"你想要发送一个表情",
"表达情绪时可以选择使用",
"一般在你回复之后可以选择性使用"
]
action_require: list[str] = ["你想要发送一个表情", "表达情绪时可以选择使用", "一般在你回复之后可以选择性使用"]
associated_types: list[str] = ["emoji"]

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@@ -59,7 +59,6 @@ def init_prompt():
"simple_planner_prompt",
)
Prompt(
"""
动作名称:{action_name}
@@ -192,7 +191,6 @@ class ActionPlanner(BasePlanner):
reasoning = f"LLM 请求失败,你的模型出现问题: {req_e}"
action = "no_reply"
if llm_content:
try:
fixed_json_string = repair_json(llm_content)
@@ -233,9 +231,7 @@ class ActionPlanner(BasePlanner):
reasoning = extracted_reasoning
except Exception as json_e:
logger.warning(
f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'"
)
logger.warning(f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'")
traceback.print_exc()
reasoning = f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_reply'."
action = "no_reply"

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@@ -195,7 +195,6 @@ class DefaultReplyer:
await self._create_thinking_message(anchor_message, thinking_id)
try:
has_sent_something = False
sent_msg_list = []
@@ -209,7 +208,6 @@ class DefaultReplyer:
else:
logger.warning(f"{self.log_prefix} 没有找到合适表情")
if reply:
with Timer("发送表情", cycle_timers):
sent_msg_list = await self.send_response_messages(
@@ -231,8 +229,6 @@ class DefaultReplyer:
traceback.print_exc()
return False, None
async def reply(
self,
# in_mind_reply: str,

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@@ -510,11 +510,15 @@ class MemoryManager:
# 如果有摘要信息,添加到提示中
if summary1:
prompt += f"记忆1主题{summary1['brief']}\n"
prompt += "记忆1关键要点\n" + "\n".join([f"- {point}" for point in summary1.get("key_points", [])]) + "\n\n"
prompt += (
"记忆1关键要点\n" + "\n".join([f"- {point}" for point in summary1.get("key_points", [])]) + "\n\n"
)
if summary2:
prompt += f"记忆2主题{summary2['brief']}\n"
prompt += "记忆2关键要点\n" + "\n".join([f"- {point}" for point in summary2.get("key_points", [])]) + "\n\n"
prompt += (
"记忆2关键要点\n" + "\n".join([f"- {point}" for point in summary2.get("key_points", [])]) + "\n\n"
)
# 添加记忆原始内容
prompt += f"""

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@@ -184,9 +184,7 @@ class ImageManager:
return f"[图片:{cached_description}]"
# 调用AI获取描述
prompt = (
"请用中文描述这张图片的内容。如果有文字请把文字都描述出来请留意其主题直观感受以及是否有擦边色情内容。最多100个字。"
)
prompt = "请用中文描述这张图片的内容。如果有文字请把文字都描述出来请留意其主题直观感受以及是否有擦边色情内容。最多100个字。"
description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
if description is None:

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@@ -90,7 +90,9 @@ class TelemetryHeartBeatTask(AsyncTask):
else:
logger.error("无效的服务端响应")
else:
logger.error(f"请求UUID失败不过你还是可以正常使用麦麦状态码: {response.status_code}, 响应内容: {response.text}")
logger.error(
f"请求UUID失败不过你还是可以正常使用麦麦状态码: {response.status_code}, 响应内容: {response.text}"
)
# 请求失败,重试次数+1
try_count += 1

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@@ -79,7 +79,11 @@ class ConfigBase:
if field_origin_type is list:
# 如果列表元素类型是ConfigBase的子类则对每个元素调用from_dict
if field_type_args and isinstance(field_type_args[0], type) and issubclass(field_type_args[0], ConfigBase):
if (
field_type_args
and isinstance(field_type_args[0], type)
and issubclass(field_type_args[0], ConfigBase)
):
return [field_type_args[0].from_dict(item) for item in value]
return [cls._convert_field(item, field_type_args[0]) for item in value]
elif field_origin_type is set: