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feat：记录使用的表达方式

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This commit is contained in:
SengokuCola
2025-08-12 21:44:35 +08:00
parent 04bd05c1fe
commit 4ffcc61f4b
12 changed files with 102 additions and 363 deletions
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41
src/chat/replyer/default_generator.py
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@@ -162,7 +162,7 @@ class DefaultReplyer:
from_plugin: bool = True,
stream_id: Optional[str] = None,
reply_message: Optional[Dict[str, Any]] = None,
) -> Tuple[bool, Optional[Dict[str, Any]], Optional[str]]:
) -> Tuple[bool, Optional[Dict[str, Any]], Optional[str], List[Dict[str, Any]]]:
# sourcery skip: merge-nested-ifs
"""
回复器 (Replier): 负责生成回复文本的核心逻辑。
@@ -186,7 +186,7 @@ class DefaultReplyer:
try:
# 3. 构建 Prompt
with Timer("构建Prompt", {}): # 内部计时器，可选保留
prompt = await self.build_prompt_reply_context(
prompt,selected_expressions = await self.build_prompt_reply_context(
extra_info=extra_info,
available_actions=available_actions,
choosen_actions=choosen_actions,
@@ -197,7 +197,7 @@ class DefaultReplyer:
if not prompt:
logger.warning("构建prompt失败，跳过回复生成")
return False, None, None
return False, None, None, []
from src.plugin_system.core.events_manager import events_manager
if not from_plugin:
@@ -229,16 +229,16 @@ class DefaultReplyer:
except Exception as llm_e:
# 精简报错信息
logger.error(f"LLM 生成失败: {llm_e}")
return False, None, prompt # LLM 调用失败则无法生成回复
return False, None, prompt, selected_expressions # LLM 调用失败则无法生成回复
return True, llm_response, prompt
return True, llm_response, prompt, selected_expressions
except UserWarning as uw:
raise uw
except Exception as e:
logger.error(f"回复生成意外失败: {e}")
traceback.print_exc()
return False, None, prompt
return False, None, prompt, selected_expressions
async def rewrite_reply_with_context(
self,
@@ -302,7 +302,7 @@ class DefaultReplyer:
return person.build_relationship(points_num=5)
async def build_expression_habits(self, chat_history: str, target: str) -> str:
async def build_expression_habits(self, chat_history: str, target: str) -> Tuple[str, List[int]]:
"""构建表达习惯块
Args:
@@ -315,11 +315,11 @@ class DefaultReplyer:
# 检查是否允许在此聊天流中使用表达
use_expression, _, _ = global_config.expression.get_expression_config_for_chat(self.chat_stream.stream_id)
if not use_expression:
return ""
return "", []
style_habits = []
# 使用从处理器传来的选中表达方式
# 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(
selected_expressions, selected_ids = await expression_selector.select_suitable_expressions_llm(
self.chat_stream.stream_id, chat_history, max_num=8, target_message=target
)
@@ -343,7 +343,7 @@ class DefaultReplyer:
)
expression_habits_block += f"{style_habits_str}\n"
return f"{expression_habits_title}\n{expression_habits_block}"
return f"{expression_habits_title}\n{expression_habits_block}", selected_ids
async def build_memory_block(self, chat_history: str, target: str) -> str:
"""构建记忆块
@@ -636,9 +636,8 @@ class DefaultReplyer:
action_descriptions += f"- {action_name}: {action_description}\n"
action_descriptions += "\n"
choosen_action_descriptions = ""
if choosen_actions:
action_descriptions += "根据聊天情况，你决定在回复的同时做以下这些动作：\n"
for action in choosen_actions:
action_name = action.get('action_type', 'unknown_action')
if action_name =="reply":
@@ -646,9 +645,11 @@ class DefaultReplyer:
action_description = action.get('reason', '无描述')
reasoning = action.get('reasoning', '无原因')
action_descriptions += f"- {action_name}: {action_description}，原因：{reasoning}\n"
choosen_action_descriptions += f"- {action_name}: {action_description}，原因：{reasoning}\n"
if choosen_action_descriptions:
action_descriptions += "根据聊天情况，你决定在回复的同时做以下这些动作：\n"
action_descriptions += choosen_action_descriptions
return action_descriptions
@@ -661,7 +662,7 @@ class DefaultReplyer:
choosen_actions: Optional[List[Dict[str, Any]]] = None,
enable_tool: bool = True,
reply_message: Optional[Dict[str, Any]] = None,
) -> str:
) -> Tuple[str, List[int]]:
"""
构建回复器上下文
@@ -759,7 +760,7 @@ class DefaultReplyer:
logger.warning(f"回复生成前信息获取耗时过长: {chinese_name} 耗时: {duration:.1f}s，请使用更快的模型")
logger.info(f"在回复前的步骤耗时: {'; '.join(timing_logs)}")
expression_habits_block = results_dict["expression_habits"]
expression_habits_block, selected_expressions = results_dict["expression_habits"]
relation_info = results_dict["relation_info"]
memory_block = results_dict["memory_block"]
tool_info = results_dict["tool_info"]
@@ -831,7 +832,7 @@ class DefaultReplyer:
reply_style=global_config.personality.reply_style,
keywords_reaction_prompt=keywords_reaction_prompt,
moderation_prompt=moderation_prompt_block,
)
),selected_expressions
else:
return await global_prompt_manager.format_prompt(
"replyer_prompt",
@@ -852,7 +853,7 @@ class DefaultReplyer:
reply_style=global_config.personality.reply_style,
keywords_reaction_prompt=keywords_reaction_prompt,
moderation_prompt=moderation_prompt_block,
)
),selected_expressions
async def build_prompt_rewrite_context(
self,
@@ -860,7 +861,7 @@ class DefaultReplyer:
reason: str,
reply_to: str,
reply_message: Optional[Dict[str, Any]] = None,
) -> str: # sourcery skip: merge-else-if-into-elif, remove-redundant-if
) -> Tuple[str, List[int]]: # sourcery skip: merge-else-if-into-elif, remove-redundant-if
chat_stream = self.chat_stream
chat_id = chat_stream.stream_id
is_group_chat = bool(chat_stream.group_info)
@@ -893,7 +894,7 @@ class DefaultReplyer:
)
# 并行执行2个构建任务
expression_habits_block, relation_info = await asyncio.gather(
(expression_habits_block, selected_expressions), relation_info = await asyncio.gather(
self.build_expression_habits(chat_talking_prompt_half, target),
self.build_relation_info(sender, target),
)