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better：优化表达方式选择

pull/1359/head
SengokuCola 2025-11-16 11:35:36 +08:00
parent bdb69dba83
commit 68916d1fcb
4 changed files with 32 additions and 14 deletions
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7
src/chat/replyer/group_generator.py
View File

@ -227,13 +227,14 @@ class DefaultReplyer:
traceback.print_exc()
return False, llm_response
async def build_expression_habits(self, chat_history: str, target: str) -> Tuple[str, List[int]]:
async def build_expression_habits(self, chat_history: str, target: str, reply_reason: str = "") -> Tuple[str, List[int]]:
# sourcery skip: for-append-to-extend
"""构建表达习惯块
Args:
chat_history: 聊天历史记录
target: 目标消息内容
reply_reason: planner给出的回复理由
Returns:
str: 表达习惯信息字符串
@ -246,7 +247,7 @@ class DefaultReplyer:
# 使用从处理器传来的选中表达方式
# 使用模型预测选择表达方式
selected_expressions, selected_ids = await expression_selector.select_suitable_expressions(
self.chat_stream.stream_id, chat_history, max_num=8, target_message=target
self.chat_stream.stream_id, chat_history, max_num=8, target_message=target, reply_reason=reply_reason
)
if selected_expressions:
@ -787,7 +788,7 @@ class DefaultReplyer:
# 并行执行七个构建任务
task_results = await asyncio.gather(
self._time_and_run_task(
self.build_expression_habits(chat_talking_prompt_short, target), "expression_habits"
self.build_expression_habits(chat_talking_prompt_short, target, reply_reason), "expression_habits"
),
self._time_and_run_task(
self.build_tool_info(chat_talking_prompt_short, sender, target, enable_tool=enable_tool), "tool_info"

7
src/chat/replyer/private_generator.py
View File

@ -241,13 +241,14 @@ class PrivateReplyer:
return f"{sender_relation}"
async def build_expression_habits(self, chat_history: str, target: str) -> Tuple[str, List[int]]:
async def build_expression_habits(self, chat_history: str, target: str, reply_reason: str = "") -> Tuple[str, List[int]]:
# sourcery skip: for-append-to-extend
"""构建表达习惯块
Args:
chat_history: 聊天历史记录
target: 目标消息内容
reply_reason: planner给出的回复理由
Returns:
str: 表达习惯信息字符串
@ -260,7 +261,7 @@ class PrivateReplyer:
# 使用从处理器传来的选中表达方式
# 使用模型预测选择表达方式
selected_expressions, selected_ids = await expression_selector.select_suitable_expressions(
self.chat_stream.stream_id, chat_history, max_num=8, target_message=target
self.chat_stream.stream_id, chat_history, max_num=8, target_message=target, reply_reason=reply_reason
)
if selected_expressions:
@ -706,7 +707,7 @@ class PrivateReplyer:
# 并行执行八个构建任务
task_results = await asyncio.gather(
self._time_and_run_task(
self.build_expression_habits(chat_talking_prompt_short, target), "expression_habits"
self.build_expression_habits(chat_talking_prompt_short, target, reply_reason), "expression_habits"
),
self._time_and_run_task(self.build_relation_info(chat_talking_prompt_short, sender), "relation_info"),
self._time_and_run_task(

7
src/chat/utils/chat_history_summarizer.py
View File

@ -89,9 +89,6 @@ class ChatHistorySummarizer:
current_time = time.time()
try:
logger.info(
f"{self.log_prefix} 开始处理聊天概括，时间窗口: {self.last_check_time:.2f} -> {current_time:.2f}"
)
# 获取从上次检查时间到当前时间的新消息
new_messages = message_api.get_messages_by_time_in_chat(
chat_id=self.chat_id,
@ -109,6 +106,10 @@ class ChatHistorySummarizer:
await self._check_and_package(current_time)
self.last_check_time = current_time
return
logger.info(
f"{self.log_prefix} 开始处理聊天概括，时间窗口: {self.last_check_time:.2f} -> {current_time:.2f}"
)
# 有新消息，更新最后检查时间
self.last_check_time = current_time

25
src/express/expression_selector.py
View File

@ -27,10 +27,11 @@ def init_prompt():
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，最多{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）
3. 情境与当前语境的匹配度
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）
3.情境与当前语境的匹配度
{target_message_extra_block}
{reply_reason_block}
请以JSON格式输出，只需要输出选中的情境编号：
例如：
@ -163,6 +164,7 @@ class ExpressionSelector:
chat_info: str,
max_num: int = 10,
target_message: Optional[str] = None,
reply_reason: Optional[str] = None,
) -> Tuple[List[Dict[str, Any]], List[int]]:
"""
选择适合的表达方式（使用classic模式：随机选择+LLM选择）
@ -172,6 +174,7 @@ class ExpressionSelector:
chat_info: 聊天内容信息
max_num: 最大选择数量
target_message: 目标消息内容
reply_reason: planner给出的回复理由
Returns:
Tuple[List[Dict[str, Any]], List[int]]: 选中的表达方式列表和ID列表
@ -183,7 +186,7 @@ class ExpressionSelector:
# 使用classic模式（随机选择+LLM选择）
logger.debug(f"使用classic模式为聊天流 {chat_id} 选择表达方式")
return await self._select_expressions_classic(chat_id, chat_info, max_num, target_message)
return await self._select_expressions_classic(chat_id, chat_info, max_num, target_message, reply_reason)
async def _select_expressions_classic(
self,
@ -191,6 +194,7 @@ class ExpressionSelector:
chat_info: str,
max_num: int = 10,
target_message: Optional[str] = None,
reply_reason: Optional[str] = None,
) -> Tuple[List[Dict[str, Any]], List[int]]:
"""
classic模式：随机选择+LLM选择
@ -200,6 +204,7 @@ class ExpressionSelector:
chat_info: 聊天内容信息
max_num: 最大选择数量
target_message: 目标消息内容
reply_reason: planner给出的回复理由
Returns:
Tuple[List[Dict[str, Any]], List[int]]: 选中的表达方式列表和ID列表
@ -229,12 +234,18 @@ class ExpressionSelector:
all_situations_str = "\n".join(all_situations)
if target_message:
target_message_str = f"，现在你想要回复消息：{target_message}"
target_message_str = f"，现在你想要对上面的这条消息进行恢复：“{target_message}”"
target_message_extra_block = "4.考虑你要回复的目标消息"
else:
target_message_str = ""
target_message_extra_block = ""
# 构建reply_reason块
if reply_reason:
reply_reason_block = f"5.考虑你的回复理由：{reply_reason}"
else:
reply_reason_block = ""
# 3. 构建prompt（只包含情境，不包含完整的表达方式）
prompt = (await global_prompt_manager.get_prompt_async("expression_evaluation_prompt")).format(
bot_name=global_config.bot.nickname,
@ -243,11 +254,15 @@ class ExpressionSelector:
max_num=max_num,
target_message=target_message_str,
target_message_extra_block=target_message_extra_block,
reply_reason_block=reply_reason_block,
)
# 4. 调用LLM
content, (reasoning_content, model_name, _) = await self.llm_model.generate_response_async(prompt=prompt)
print(prompt)
if not content:
logger.warning("LLM返回空结果")
return [], []