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数据库的信息重构为dataclass

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This commit is contained in:
UnCLAS-Prommer
2025-08-17 17:11:32 +08:00
parent d74beef4b4
commit 3481234d2b
18 changed files with 243 additions and 206 deletions
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106
src/chat/replyer/default_generator.py
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@@ -91,7 +91,7 @@ def init_prompt():
""",
"replyer_prompt",
)
Prompt(
"""
{expression_habits_block}{tool_info_block}
@@ -116,7 +116,6 @@ def init_prompt():
""",
"replyer_self_prompt",
)
Prompt(
"""
@@ -179,7 +178,7 @@ class DefaultReplyer:
Returns:
Tuple[bool, Optional[Dict[str, Any]], Optional[str]]: (是否成功, 生成的回复, 使用的prompt)
"""
prompt = None
selected_expressions = None
if available_actions is None:
@@ -187,7 +186,7 @@ class DefaultReplyer:
try:
# 3. 构建 Prompt
with Timer("构建Prompt", {}): # 内部计时器，可选保留
prompt,selected_expressions = 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,
@@ -294,12 +293,12 @@ class DefaultReplyer:
async def build_relation_info(self, sender: str, target: str):
if not global_config.relationship.enable_relationship:
return ""
if sender == global_config.bot.nickname:
return ""
# 获取用户ID
person = Person(person_name = sender)
person = Person(person_name=sender)
if not is_person_known(person_name=sender):
logger.warning(f"未找到用户 {sender} 的ID，跳过信息提取")
return f"你完全不认识{sender}，不理解ta的相关信息。"
@@ -307,6 +306,7 @@ class DefaultReplyer:
return person.build_relationship(points_num=5)
async def build_expression_habits(self, chat_history: str, target: str) -> Tuple[str, List[int]]:
# sourcery skip: for-append-to-extend
"""构建表达习惯块
Args:
@@ -359,7 +359,7 @@ class DefaultReplyer:
Returns:
str: 记忆信息字符串
"""
if not global_config.memory.enable_memory:
return ""
@@ -368,7 +368,6 @@ class DefaultReplyer:
running_memories = await self.memory_activator.activate_memory_with_chat_history(
target_message=target, chat_history_prompt=chat_history
)
if global_config.memory.enable_instant_memory:
asyncio.create_task(self.instant_memory.create_and_store_memory(chat_history))
@@ -379,10 +378,9 @@ class DefaultReplyer:
if not running_memories:
return ""
memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
for running_memory in running_memories:
keywords,content = running_memory
keywords, content = running_memory
memory_str += f"- {keywords}：{content}\n"
if instant_memory:
@@ -405,7 +403,6 @@ class DefaultReplyer:
if not enable_tool:
return ""
try:
# 使用工具执行器获取信息
tool_results, _, _ = await self.tool_executor.execute_from_chat_message(
@@ -559,16 +556,18 @@ class DefaultReplyer:
# 检查最新五条消息中是否包含bot自己说的消息
latest_5_messages = core_dialogue_list[-5:] if len(core_dialogue_list) >= 5 else core_dialogue_list
has_bot_message = any(str(msg.get("user_id")) == bot_id for msg in latest_5_messages)
# logger.info(f"最新五条消息：{latest_5_messages}")
# logger.info(f"最新五条消息中是否包含bot自己说的消息：{has_bot_message}")
# 如果最新五条消息中不包含bot的消息，则返回空字符串
if not has_bot_message:
core_dialogue_prompt = ""
else:
core_dialogue_list = core_dialogue_list[-int(global_config.chat.max_context_size * 0.6) :] # 限制消息数量
core_dialogue_list = core_dialogue_list[
-int(global_config.chat.max_context_size * 0.6) :
] # 限制消息数量
core_dialogue_prompt_str = build_readable_messages(
core_dialogue_list,
replace_bot_name=True,
@@ -630,12 +629,12 @@ class DefaultReplyer:
mai_think.sender = sender
mai_think.target = target
return mai_think
async def build_actions_prompt(self, available_actions, choosen_actions: Optional[List[Dict[str, Any]]] = None) -> str:
"""构建动作提示
"""
async def build_actions_prompt(
self, available_actions, choosen_actions: Optional[List[Dict[str, Any]]] = None
) -> str:
"""构建动作提示"""
action_descriptions = ""
if available_actions:
action_descriptions = "你可以做以下这些动作：\n"
@@ -643,25 +642,24 @@ class DefaultReplyer:
action_description = action_info.description
action_descriptions += f"- {action_name}: {action_description}\n"
action_descriptions += "\n"
choosen_action_descriptions = ""
if choosen_actions:
for action in choosen_actions:
action_name = action.get('action_type', 'unknown_action')
if action_name =="reply":
action_name = action.get("action_type", "unknown_action")
if action_name == "reply":
continue
action_description = action.get('reason', '无描述')
reasoning = action.get('reasoning', '无原因')
action_description = action.get("reason", "无描述")
reasoning = action.get("reasoning", "无原因")
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
async def build_prompt_reply_context(
self,
extra_info: str = "",
@@ -691,41 +689,44 @@ class DefaultReplyer:
chat_id = chat_stream.stream_id
is_group_chat = bool(chat_stream.group_info)
platform = chat_stream.platform
if reply_message:
user_id = reply_message.get("user_id","")
user_id = reply_message.get("user_id", "")
person = Person(platform=platform, user_id=user_id)
person_name = person.person_name or user_id
sender = person_name
target = reply_message.get('processed_plain_text')
target = reply_message.get("processed_plain_text")
else:
person_name = "用户"
sender = "用户"
target = "消息"
if global_config.mood.enable_mood:
chat_mood = mood_manager.get_mood_by_chat_id(chat_id)
mood_prompt = chat_mood.mood_state
else:
mood_prompt = ""
target = replace_user_references_sync(target, chat_stream.platform, replace_bot_name=True)
# TODO: 修复！
message_list_before_now_long = get_raw_msg_before_timestamp_with_chat(
chat_id=chat_id,
timestamp=time.time(),
limit=global_config.chat.max_context_size * 1,
)
temp_msg_list_before_long = [msg.__dict__ for msg in message_list_before_now_long]
# TODO: 修复！
message_list_before_short = get_raw_msg_before_timestamp_with_chat(
chat_id=chat_id,
timestamp=time.time(),
limit=int(global_config.chat.max_context_size * 0.33),
)
temp_msg_list_before_short = [msg.__dict__ for msg in message_list_before_short]
chat_talking_prompt_short = build_readable_messages(
message_list_before_short,
temp_msg_list_before_short,
replace_bot_name=True,
merge_messages=False,
timestamp_mode="relative",
@@ -739,12 +740,12 @@ class DefaultReplyer:
self.build_expression_habits(chat_talking_prompt_short, target), "expression_habits"
),
self._time_and_run_task(self.build_relation_info(sender, target), "relation_info"),
self._time_and_run_task(self.build_memory_block(message_list_before_short, target), "memory_block"),
self._time_and_run_task(self.build_memory_block(temp_msg_list_before_short, target), "memory_block"),
self._time_and_run_task(
self.build_tool_info(chat_talking_prompt_short, sender, target, enable_tool=enable_tool), "tool_info"
),
self._time_and_run_task(self.get_prompt_info(chat_talking_prompt_short, sender, target), "prompt_info"),
self._time_and_run_task(self.build_actions_prompt(available_actions,choosen_actions), "actions_info"),
self._time_and_run_task(self.build_actions_prompt(available_actions, choosen_actions), "actions_info"),
)
# 任务名称中英文映射
@@ -760,7 +761,7 @@ class DefaultReplyer:
# 处理结果
timing_logs = []
results_dict = {}
almost_zero_str = ""
for name, result, duration in task_results:
results_dict[name] = result
@@ -768,7 +769,7 @@ class DefaultReplyer:
if duration < 0.01:
almost_zero_str += f"{chinese_name},"
continue
timing_logs.append(f"{chinese_name}: {duration:.1f}s")
if duration > 8:
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")
@@ -791,9 +792,7 @@ class DefaultReplyer:
identity_block = await get_individuality().get_personality_block()
moderation_prompt_block = (
"请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
)
moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
if sender:
if is_group_chat:
@@ -801,7 +800,9 @@ class DefaultReplyer:
f"现在{sender}说的:{target}。引起了你的注意，你想要在群里发言或者回复这条消息。原因是{reply_reason}"
)
else: # private chat
reply_target_block = f"现在{sender}说的:{target}。引起了你的注意，针对这条消息回复。原因是{reply_reason}"
reply_target_block = (
f"现在{sender}说的:{target}。引起了你的注意，针对这条消息回复。原因是{reply_reason}"
)
else:
reply_target_block = ""
@@ -821,10 +822,9 @@ class DefaultReplyer:
# "chat_target_private2", sender_name=chat_target_name
# )
# 构建分离的对话 prompt
core_dialogue_prompt, background_dialogue_prompt = self.build_s4u_chat_history_prompts(
message_list_before_now_long, user_id, sender
temp_msg_list_before_long, user_id, sender
)
if global_config.bot.qq_account == user_id and platform == global_config.bot.platform:
@@ -846,7 +846,7 @@ class DefaultReplyer:
reply_style=global_config.personality.reply_style,
keywords_reaction_prompt=keywords_reaction_prompt,
moderation_prompt=moderation_prompt_block,
),selected_expressions
), selected_expressions
else:
return await global_prompt_manager.format_prompt(
"replyer_prompt",
@@ -867,7 +867,7 @@ class DefaultReplyer:
reply_style=global_config.personality.reply_style,
keywords_reaction_prompt=keywords_reaction_prompt,
moderation_prompt=moderation_prompt_block,
),selected_expressions
), selected_expressions
async def build_prompt_rewrite_context(
self,
@@ -898,8 +898,10 @@ class DefaultReplyer:
timestamp=time.time(),
limit=min(int(global_config.chat.max_context_size * 0.33), 15),
)
# TODO: 修复！
temp_msg_list_before_now_half = [msg.__dict__ for msg in message_list_before_now_half]
chat_talking_prompt_half = build_readable_messages(
message_list_before_now_half,
temp_msg_list_before_now_half,
replace_bot_name=True,
merge_messages=False,
timestamp_mode="relative",
@@ -912,7 +914,6 @@ class DefaultReplyer:
self.build_expression_habits(chat_talking_prompt_half, target),
self.build_relation_info(sender, target),
)
keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
@@ -1024,7 +1025,9 @@ class DefaultReplyer:
else:
logger.debug(f"\n{prompt}\n")
content, (reasoning_content, model_name, tool_calls) = await self.express_model.generate_response_async(prompt)
content, (reasoning_content, model_name, tool_calls) = await self.express_model.generate_response_async(
prompt
)
logger.debug(f"replyer生成内容: {content}")
return content, reasoning_content, model_name, tool_calls
@@ -1034,7 +1037,6 @@ class DefaultReplyer:
start_time = time.time()
from src.plugins.built_in.knowledge.lpmm_get_knowledge import SearchKnowledgeFromLPMMTool
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
# 从LPMM知识库获取知识
try: