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🤖 自动格式化代码 [skip ci]

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This commit is contained in:
github-actions[bot]
2025-06-22 17:16:36 +00:00
parent 8b1ec538ed
commit 02950ab538
6 changed files with 83 additions and 90 deletions
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3
src/chat/focus_chat/heartFC_chat.py
View File

@@ -660,7 +660,6 @@ class HeartFChatting:
}
with Timer("执行动作", cycle_timers):
action_type, action_data, reasoning = (
plan_result.get("action_result", {}).get("action_type", "error"),
plan_result.get("action_result", {}).get("action_data", {}),
@@ -675,7 +674,7 @@ class HeartFChatting:
action_str = action_type
logger.debug(f"{self.log_prefix} 麦麦想要：'{action_str}'")
success, reply_text, command = await self._handle_action(
action_type, reasoning, action_data, cycle_timers, thinking_id
)

4
src/chat/focus_chat/info/obs_info.py
View File

@@ -80,7 +80,7 @@ class ObsInfo(InfoBase):
chat_target (str): 聊天目标，可以是 "private"（私聊）、"group"（群聊）或 "other"（其他）
"""
self.data["chat_target"] = chat_target
def set_chat_id(self, chat_id: str) -> None:
"""设置聊天ID
@@ -88,7 +88,7 @@ class ObsInfo(InfoBase):
chat_id (str): 聊天ID
"""
self.data["chat_id"] = chat_id
def get_chat_id(self) -> Optional[str]:
"""获取聊天ID

2
src/chat/focus_chat/info_processors/expression_selector_processor.py
View File

@@ -149,7 +149,7 @@ class ExpressionSelectorProcessor(BaseProcessor):
if observations:
for observation in observations:
if isinstance(observation, ChattingObservation):
# chat_info = observation.get_observe_info()
# chat_info = observation.get_observe_info()
chat_info = observation.talking_message_str_truncate_short
break

39
src/chat/focus_chat/planners/planner_simple.py
View File

@@ -42,7 +42,7 @@ def init_prompt():
""",
"simple_planner_prompt",
)
Prompt(
"""
{time_block}
@@ -62,19 +62,19 @@ def init_prompt():
"simple_planner_prompt_private",
)
# Prompt(
# """
# 动作：{action_name}
# 该动作的描述：{action_description}
# 使用该动作的场景：
# {action_require}
# 输出要求：
# {{
# "action": "{action_name}",{action_parameters}
# }}
# """,
# "action_prompt",
# )
# Prompt(
# """
# 动作：{action_name}
# 该动作的描述：{action_description}
# 使用该动作的场景：
# {action_require}
# 输出要求：
# {{
# "action": "{action_name}",{action_parameters}
# }}
# """,
# "action_prompt",
# )
Prompt(
"""
{action_require}
@@ -84,7 +84,7 @@ def init_prompt():
""",
"action_prompt",
)
Prompt(
"""
{action_require}
@@ -96,7 +96,6 @@ def init_prompt():
)
class ActionPlanner(BasePlanner):
def __init__(self, log_prefix: str, action_manager: ActionManager):
super().__init__(log_prefix, action_manager)
@@ -141,7 +140,7 @@ class ActionPlanner(BasePlanner):
relation_info = ""
selected_expressions = []
chat_id = None # 添加chat_id变量
for info in all_plan_info:
if isinstance(info, ObsInfo):
observed_messages = info.get_talking_message()
@@ -170,7 +169,9 @@ class ActionPlanner(BasePlanner):
# 如果获取成功，更新is_group_chat
if is_group_chat_updated is not None:
is_group_chat = is_group_chat_updated
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}")
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}"
)
except Exception as e:
logger.warning(f"{self.log_prefix}获取聊天目标信息失败: {e}")
chat_target_info = None
@@ -372,7 +373,7 @@ class ActionPlanner(BasePlanner):
action_options_block = ""
# 根据聊天类型选择不同的动作prompt模板
action_template_name = "action_prompt_private" if not is_group_chat else "action_prompt"
for using_actions_name, using_actions_info in current_available_actions.items():
using_action_prompt = await global_prompt_manager.get_prompt_async(action_template_name)

4
src/chat/heart_flow/observation/chatting_observation.py
View File

@@ -232,11 +232,11 @@ class ChattingObservation(Observation):
truncate=True,
show_actions=True,
)
# 构建简短版本 - 使用最新一半的消息
half_count = len(self.talking_message) // 2
recent_messages = self.talking_message[-half_count:] if half_count > 0 else self.talking_message
self.talking_message_str_short = build_readable_messages(
messages=recent_messages,
timestamp_mode="lite",

121
src/plugins/built_in/core_actions/plugin.py
View File

@@ -56,8 +56,6 @@ class ReplyAction(BaseAction):
logger.info(f"{self.log_prefix} 决定回复: {self.reasoning}")
start_time = self.action_data.get("loop_start_time", time.time())
try:
success, reply_set = await generator_api.generate_reply(
@@ -68,7 +66,6 @@ class ReplyAction(BaseAction):
is_group=self.is_group,
)
# 检查从start_time以来的新消息数量
# 获取动作触发时间或使用默认值
current_time = time.time()
@@ -89,15 +86,14 @@ class ReplyAction(BaseAction):
data = reply_seg[1]
if not first_replyed:
if need_reply:
await self.send_text(content=data, reply_to=self.action_data.get("reply_to", ""),typing=False)
await self.send_text(content=data, reply_to=self.action_data.get("reply_to", ""), typing=False)
first_replyed = True
else:
await self.send_text(content=data,typing=False)
await self.send_text(content=data, typing=False)
first_replyed = True
else:
await self.send_text(content=data,typing=True)
await self.send_text(content=data, typing=True)
reply_text += data
# 存储动作记录
await self.store_action_info(
@@ -118,7 +114,7 @@ class ReplyAction(BaseAction):
class NoReplyAction(BaseAction):
"""不回复动作，使用智能判断机制决定何时结束等待
新的等待逻辑：
- 每0.2秒检查是否有新消息（提高响应性）
- 如果累计消息数量达到阈值（默认20条），直接结束等待
@@ -139,13 +135,13 @@ class NoReplyAction(BaseAction):
# 连续no_reply计数器
_consecutive_count = 0
# LLM判断的最小间隔时间
_min_judge_interval = 1.0 # 最快1秒一次LLM判断
# 自动结束的消息数量阈值
_auto_exit_message_count = 20 # 累计20条消息自动结束
# 最大等待超时时间
_max_timeout = 1200 # 1200秒
@@ -161,7 +157,7 @@ class NoReplyAction(BaseAction):
async def execute(self) -> Tuple[bool, str]:
"""执行不回复动作，有新消息时进行判断，但最快1秒一次"""
import asyncio
try:
# 增加连续计数
NoReplyAction._consecutive_count += 1
@@ -172,33 +168,30 @@ class NoReplyAction(BaseAction):
last_judge_time = 0 # 上次进行LLM判断的时间
min_judge_interval = self._min_judge_interval # 最小判断间隔，从配置获取
check_interval = 0.2 # 检查新消息的间隔，设为0.2秒提高响应性
# 获取no_reply开始时的上下文消息（5条），用于后续记录
context_messages = message_api.get_messages_by_time_in_chat(
chat_id=self.chat_id,
start_time=start_time - 300, # 获取开始前5分钟内的消息
end_time=start_time,
limit=5,
limit_mode="latest"
limit_mode="latest",
)
# 构建上下文字符串
context_str = ""
if context_messages:
context_str = message_api.build_readable_messages(
messages=context_messages,
timestamp_mode="normal_no_YMD",
truncate=False,
show_actions=False
messages=context_messages, timestamp_mode="normal_no_YMD", truncate=False, show_actions=False
)
context_str = f"当时选择no_reply前的聊天上下文：\n{context_str}\n"
logger.info(f"{self.log_prefix} 选择不回复(第{count}次)，开始智能等待，原因: {reason}")
while True:
current_time = time.time()
elapsed_time = current_time - start_time
# 检查是否超时
if elapsed_time >= self._max_timeout:
logger.info(f"{self.log_prefix} 达到最大等待时间{self._max_timeout}秒，结束等待")
@@ -210,12 +203,12 @@ class NoReplyAction(BaseAction):
action_done=True,
)
return True, exit_reason
# 检查是否有新消息
new_message_count = message_api.count_new_messages(
chat_id=self.chat_id, start_time=start_time, end_time=current_time
)
# 如果累计消息数量达到阈值，直接结束等待
if new_message_count >= self._auto_exit_message_count:
logger.info(f"{self.log_prefix} 累计消息数量达到{new_message_count}条，直接结束等待")
@@ -227,31 +220,28 @@ class NoReplyAction(BaseAction):
action_done=True,
)
return True, f"累计消息数量达到{new_message_count}条，直接结束等待 (等待时间: {elapsed_time:.1f}秒)"
# 如果有新消息且距离上次判断>=1秒，进行LLM判断
if new_message_count > 0 and (current_time - last_judge_time) >= min_judge_interval:
logger.info(f"{self.log_prefix} 检测到{new_message_count}条新消息，进行智能判断...")
# 获取最近的消息内容用于判断
recent_messages = message_api.get_messages_by_time_in_chat(
chat_id=self.chat_id,
chat_id=self.chat_id,
start_time=start_time,
end_time=current_time,
)
if recent_messages:
# 使用message_api构建可读的消息字符串
messages_text = message_api.build_readable_messages(
messages=recent_messages,
timestamp_mode="normal_no_YMD",
truncate=False,
show_actions=False
messages=recent_messages, timestamp_mode="normal_no_YMD", truncate=False, show_actions=False
)
# 参考simple_planner构建更完整的判断信息
# 获取时间信息
time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
# 获取身份信息
bot_name = global_config.bot.nickname
bot_nickname = ""
@@ -259,7 +249,7 @@ class NoReplyAction(BaseAction):
bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
bot_core_personality = global_config.personality.personality_core
identity_block = f"你的名字是{bot_name}{bot_nickname}，你{bot_core_personality}"
# 构建判断上下文
judge_prompt = f"""
{time_block}
@@ -282,35 +272,35 @@ class NoReplyAction(BaseAction):
判断：需要回复/不需要回复
理由：[说明你的判断理由]
"""
try:
# 获取可用的模型配置
available_models = llm_api.get_available_models()
# 使用 utils_small 模型
small_model = getattr(available_models, 'utils_small', None)
small_model = getattr(available_models, "utils_small", None)
if small_model:
# 使用小模型进行判断
success, response, reasoning, model_name = await llm_api.generate_with_model(
prompt=judge_prompt,
model_config=small_model,
request_type="plugin.no_reply_judge",
temperature=0.7 # 降低温度，提高判断的一致性
temperature=0.7, # 降低温度，提高判断的一致性
)
# 更新上次判断时间
last_judge_time = time.time()
if success and response:
response = response.strip()
logger.info(f"{self.log_prefix} 模型({model_name})原始判断结果: {response}")
# 解析LLM响应，提取判断结果和理由
judge_result, reason = self._parse_llm_judge_response(response)
logger.info(f"{self.log_prefix} 解析结果 - 判断: {judge_result}, 理由: {reason}")
if judge_result == "需要回复":
logger.info(f"{self.log_prefix} 模型判断需要回复，结束等待")
full_prompt = f"你的想法是：{reason}，你思考是否要进行回复"
@@ -329,15 +319,15 @@ class NoReplyAction(BaseAction):
else:
logger.warning(f"{self.log_prefix} 未找到可用的模型配置，继续等待")
last_judge_time = time.time() # 即使失败也更新时间，避免频繁重试
except Exception as e:
logger.error(f"{self.log_prefix} 模型判断异常: {e}，继续等待")
last_judge_time = time.time() # 异常时也更新时间，避免频繁重试
# 每10秒输出一次等待状态
if int(elapsed_time) % 10 == 0 and int(elapsed_time) > 0:
logger.info(f"{self.log_prefix} 已等待{elapsed_time:.0f}秒，继续监听...")
# 短暂等待后继续检查
await asyncio.sleep(check_interval)
@@ -366,49 +356,49 @@ class NoReplyAction(BaseAction):
def _parse_llm_judge_response(self, response: str) -> tuple[str, str]:
"""解析LLM判断响应，提取判断结果和理由
Args:
response: LLM的原始响应
Returns:
tuple: (判断结果, 理由)
"""
try:
lines = response.strip().split('\n')
lines = response.strip().split("\n")
judge_result = "不需要回复" # 默认值
reason = "解析失败，使用默认判断"
for line in lines:
line = line.strip()
if line.startswith('判断：') or line.startswith('判断:'):
if line.startswith("判断：") or line.startswith("判断:"):
# 提取判断结果
result_part = line.split('：', 1)[-1] if '：' in line else line.split(':', 1)[-1]
result_part = line.split("：", 1)[-1] if "：" in line else line.split(":", 1)[-1]
result_part = result_part.strip()
if "需要回复" in result_part:
judge_result = "需要回复"
elif "不需要回复" in result_part:
judge_result = "不需要回复"
elif line.startswith('理由：') or line.startswith('理由:'):
elif line.startswith("理由：") or line.startswith("理由:"):
# 提取理由
reason_part = line.split('：', 1)[-1] if '：' in line else line.split(':', 1)[-1]
reason_part = line.split("：", 1)[-1] if "：" in line else line.split(":", 1)[-1]
reason = reason_part.strip()
# 如果没有找到标准格式，尝试简单的关键词匹配
if reason == "解析失败，使用默认判断":
if "需要回复" in response:
judge_result = "需要回复"
reason = "检测到'需要回复'关键词"
elif "不需要回复" in response:
judge_result = "不需要回复"
judge_result = "不需要回复"
reason = "检测到'不需要回复'关键词"
else:
reason = f"无法解析响应格式，原文: {response[:50]}..."
logger.debug(f"{self.log_prefix} 解析LLM响应 - 判断: {judge_result}, 理由: {reason}")
return judge_result, reason
except Exception as e:
logger.error(f"{self.log_prefix} 解析LLM响应时出错: {e}")
return "不需要回复", f"解析异常: {str(e)}"
@@ -486,7 +476,6 @@ class EmojiAction(BaseAction):
return False, f"表情发送失败: {str(e)}"
class ExitFocusChatAction(BaseAction):
"""退出专注聊天动作 - 从专注模式切换到普通模式"""
@@ -588,8 +577,12 @@ class CoreActionsPlugin(BasePlugin):
},
"no_reply": {
"max_timeout": ConfigField(type=int, default=1200, description="最大等待超时时间（秒）"),
"min_judge_interval": ConfigField(type=float, default=1.0, description="LLM判断的最小间隔时间（秒），防止过于频繁"),
"auto_exit_message_count": ConfigField(type=int, default=20, description="累计消息数量达到此阈值时自动结束等待"),
"min_judge_interval": ConfigField(
type=float, default=1.0, description="LLM判断的最小间隔时间（秒），防止过于频繁"
),
"auto_exit_message_count": ConfigField(
type=int, default=20, description="累计消息数量达到此阈值时自动结束等待"
),
"random_probability": ConfigField(
type=float, default=0.8, description="Focus模式下，随机选择不回复的概率（0.0到1.0）", example=0.8
),