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feat：添加时段talk_frequency控制

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SengokuCola
2025-06-25 17:18:43 +08:00
parent cfe5eb7d4e
commit 7b559cdc5f
11 changed files with 210 additions and 23 deletions
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13
src/chat/express/expression_selector.py
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@@ -23,7 +23,7 @@ def init_prompt():
以下是可选的表达情境：
{all_situations}
请你分析聊天内容的语境、情绪、话题类型，从上述情境中选择最适合当前聊天情境的5-10个情境。
请你分析聊天内容的语境、情绪、话题类型，从上述情境中选择最适合当前聊天情境的{min_num}-{max_num}个情境。
考虑因素包括：
1. 聊天的情绪氛围（轻松、严肃、幽默等）
2. 话题类型（日常、技术、游戏、情感等）
@@ -32,11 +32,11 @@ def init_prompt():
请以JSON格式输出，只需要输出选中的情境编号：
例如：
{{
"selected_situations": [2, 3, 5, 7, 9, 12, 15, 18, 21, 25]
"selected_situations": [2, 3, 5, 7, 19, 22, 25, 38, 39, 45, 48 , 64]
}}
例如：
{{
"selected_situations": [1, 4, 7, 9, 13, 18, 24]
"selected_situations": [1, 4, 7, 9, 23, 38, 44]
}}
请严格按照JSON格式输出，不要包含其他内容：
@@ -145,8 +145,8 @@ class ExpressionSelector:
except Exception as e:
logger.error(f"更新表达方式count失败: {e}")
async def select_suitable_expressions_llm(self, chat_id: str, chat_info: str) -> List[Dict[str, str]]:
async def select_suitable_expressions_llm(self, chat_id: str, chat_info: str, max_num: int = 10, min_num: int = 5) -> List[Dict[str, str]]:
"""使用LLM选择适合的表达方式"""
# 1. 获取35个随机表达方式（现在按权重抽取）
@@ -191,9 +191,10 @@ class ExpressionSelector:
bot_name=global_config.bot.nickname,
chat_observe_info=chat_info,
all_situations=all_situations_str,
min_num=min_num,
max_num=max_num,
)
print(prompt)
# 4. 调用LLM
try:

6
src/chat/express/exprssion_learner.py
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@@ -96,7 +96,7 @@ class ExpressionLearner:
current_chat_type = "private"
typed_chat_id = f"{platform}:{chat_stream.user_info.user_id}:{current_chat_type}"
logger.info(f"正在为 {typed_chat_id} 查找互通组...")
logger.debug(f"正在为 {typed_chat_id} 查找互通组...")
found_group = None
for group in expression_groups:
@@ -108,7 +108,7 @@ class ExpressionLearner:
break
if not found_group:
logger.info(f"未找到互通组，仅加载 {chat_id} 的表达方式")
logger.debug(f"未找到互通组，仅加载 {chat_id} 的表达方式")
if found_group:
# 从带类型的id中解析出原始id
@@ -121,7 +121,7 @@ class ExpressionLearner:
except Exception:
logger.warning(f"无法解析互通组中的ID: {item}")
chat_ids_to_load = parsed_ids
logger.info(f"将要加载以下id的表达方式: {chat_ids_to_load}")
logger.debug(f"将要加载以下id的表达方式: {chat_ids_to_load}")
learnt_style_expressions = []
learnt_grammar_expressions = []

3
src/chat/focus_chat/heartflow_message_processor.py
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@@ -180,7 +180,8 @@ class HeartFCMessageReceiver:
# 7. 日志记录
mes_name = chat.group_info.group_name if chat.group_info else "私聊"
# current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time))
logger.info(f"[{mes_name}]{userinfo.user_nickname}:{message.processed_plain_text}")
current_talk_frequency = global_config.chat.get_current_talk_frequency(chat.stream_id)
logger.info(f"[{mes_name}]{userinfo.user_nickname}:{message.processed_plain_text}[当前回复频率: {current_talk_frequency}]")
# 8. 关系处理
if global_config.relationship.enable_relationship:

4
src/chat/focus_chat/info_processors/expression_selector_processor.py
View File

@@ -71,9 +71,7 @@ class ExpressionSelectorProcessor(BaseProcessor):
try:
# LLM模式：调用LLM选择5-10个，然后随机选5个
selected_expressions = await expression_selector.select_suitable_expressions_llm(
self.subheartflow_id, chat_info
)
selected_expressions = await expression_selector.select_suitable_expressions_llm(self.subheartflow_id, chat_info, max_num=12, min_num=2)
cache_size = len(selected_expressions) if selected_expressions else 0
mode_desc = f"LLM模式（已缓存{cache_size}个）"

7
src/chat/focus_chat/info_processors/relationship_processor.py
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@@ -292,8 +292,11 @@ class PersonImpressionpProcessor(BaseProcessor):
"message_count": self._count_messages_in_timerange(potential_start_time, message_time),
}
segments.append(new_segment)
person_name = get_person_info_manager().get_value(person_id, "person_name")
logger.info(
f"{self.log_prefix} 为用户 {person_id} 创建新消息段: 时间范围 {time.strftime('%H:%M:%S', time.localtime(potential_start_time))} - {time.strftime('%H:%M:%S', time.localtime(message_time))}, 消息数: {new_segment['message_count']}"
f"{self.log_prefix} 眼熟用户 {person_name} 在 {time.strftime('%H:%M:%S', time.localtime(potential_start_time))} - {time.strftime('%H:%M:%S', time.localtime(message_time))} 之间有 {new_segment['message_count']} 条消息"
)
self._save_cache()
return
@@ -339,7 +342,7 @@ class PersonImpressionpProcessor(BaseProcessor):
"message_count": self._count_messages_in_timerange(potential_start_time, message_time),
}
segments.append(new_segment)
logger.info(f"{self.log_prefix} 为用户 {person_id} 创建新消息段（超过10条消息间隔）: {new_segment}")
logger.info(f"{self.log_prefix} 重新眼熟用户 {person_name} 创建新消息段（超过10条消息间隔）: {new_segment}")
self._save_cache()

6
src/chat/message_receive/chat_stream.py
View File

@@ -172,12 +172,12 @@ class ChatManager:
key = "_".join(components)
return hashlib.md5(key.encode()).hexdigest()
def get_stream_id(self, platform: str, chat_id: str, is_group: bool = True) -> str:
def get_stream_id(self, platform: str, id: str, is_group: bool = True) -> str:
"""获取聊天流ID"""
if is_group:
components = [platform, str(chat_id)]
components = [platform, str(id)]
else:
components = [platform, str(chat_id), "private"]
components = [platform, str(id), "private"]
key = "_".join(components)
return hashlib.md5(key.encode()).hexdigest()

2
src/chat/normal_chat/normal_chat.py
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@@ -1000,7 +1000,7 @@ class NormalChat:
"""
# --- 1. 定义参数 ---
evaluation_minutes = 10.0
target_replies_per_min = global_config.chat.talk_frequency # 目标频率：e.g. 1条/分钟
target_replies_per_min = global_config.chat.get_current_talk_frequency(self.stream_id) # 目标频率：e.g. 1条/分钟
target_replies_in_window = target_replies_per_min * evaluation_minutes # 10分钟内的目标回复数
if target_replies_in_window <= 0:

2
src/chat/normal_chat/normal_prompt.py
View File

@@ -163,7 +163,7 @@ class PromptBuilder:
show_actions=True,
)
expressions = await expression_selector.select_suitable_expressions_llm(chat_stream.stream_id, chat_talking_prompt_half)
expressions = await expression_selector.select_suitable_expressions_llm(chat_stream.stream_id, chat_talking_prompt_half, max_num=8, min_num=3)
style_habbits = []
grammar_habbits = []
if expressions: