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MaiBot
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更多的prompt独立

r-dev
UnCLAS-Prommer 2026-01-21 18:18:52 +08:00
parent 761e4c8940
commit 21bca6b030
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8 changed files with 114 additions and 84 deletions
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1
prompts/chat_target_group1.prompt 100644
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@ -0,0 +1 @@
你正在qq群里聊天，下面是群里正在聊的内容:

1
prompts/chat_target_group2.prompt 100644
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@ -0,0 +1 @@
正在群里聊天

1
prompts/chat_target_private1.prompt 100644
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@ -0,0 +1 @@
你正在和{sender_name}聊天，这是你们之前聊的内容：

1
prompts/chat_target_private2.prompt 100644
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@ -0,0 +1 @@
和{sender_name}聊天

15
src/chat/replyer/prompt/rewrite_prompt.py → prompts/default_expressor_prompt.prompt
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@ -1,15 +1,3 @@
from src.chat.utils.prompt_builder import Prompt
# from src.memory_system.memory_activator import MemoryActivator
def init_rewrite_prompt():
Prompt("你正在qq群里聊天，下面是群里正在聊的内容:", "chat_target_group1")
Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
Prompt("正在群里聊天", "chat_target_group2")
Prompt("和{sender_name}聊天", "chat_target_private2")
Prompt(
"""
{expression_habits_block}
{chat_target}
{chat_info}
@ -26,6 +14,3 @@ def init_rewrite_prompt():
{moderation_prompt}
不要输出多余内容(包括冒号和引号，表情包，emoji,at或 @等 )，只输出一条回复就好。不要思考的太长。
改写后的回复：
""",
"default_expressor_prompt",
)

10
prompts/lpmm_get_knowledge_prompt.prompt 100644
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@ -0,0 +1,10 @@
你是一个专门获取知识的助手。你的名字是{bot_name}。现在是{time_now}。
群里正在进行的聊天内容：
{chat_history}
现在，{sender}发送了内容:{target_message},你想要回复ta。
请仔细分析聊天内容，考虑以下几点：
1. 内容中是否包含需要查询信息的问题
2. 是否有明确的知识获取指令
If you need to use the search tool, please directly call the function "lpmm_search_knowledge". If you do not need to use any tool, simply output "No tool needed".

98
src/chat/replyer/group_generator.py
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@ -18,7 +18,6 @@ from src.chat.message_receive.uni_message_sender import UniversalMessageSender
from src.chat.utils.timer_calculator import Timer # <--- Import Timer
from src.chat.utils.utils import get_chat_type_and_target_info, is_bot_self
from src.prompt.prompt_manager import prompt_manager
from src.chat.utils.prompt_builder import global_prompt_manager
from src.chat.utils.chat_message_builder import (
build_readable_messages,
get_raw_msg_before_timestamp_with_chat,
@ -34,13 +33,11 @@ from src.plugin_system.apis import llm_api
from src.chat.logger.plan_reply_logger import PlanReplyLogger
from src.chat.replyer.prompt.lpmm_prompt import init_lpmm_prompt
from src.chat.replyer.prompt.rewrite_prompt import init_rewrite_prompt
from src.memory_system.memory_retrieval import init_memory_retrieval_prompt, build_memory_retrieval_prompt
from src.bw_learner.jargon_explainer import explain_jargon_in_context, retrieve_concepts_with_jargon
from src.chat.utils.common_utils import TempMethodsExpression
init_lpmm_prompt()
init_rewrite_prompt()
init_memory_retrieval_prompt()
@ -72,7 +69,7 @@ class DefaultReplyer:
from_plugin: bool = True,
stream_id: Optional[str] = None,
reply_message: Optional[DatabaseMessages] = None,
reply_time_point: Optional[float] = time.time(),
reply_time_point: float = time.time(),
think_level: int = 1,
unknown_words: Optional[List[str]] = None,
log_reply: bool = True,
@ -619,7 +616,7 @@ class DefaultReplyer:
# 默认 / context 模式：使用上下文自动匹配黑话
try:
return await explain_jargon_in_context(chat_id, messages_short, chat_talking_prompt_short)
return await explain_jargon_in_context(chat_id, messages_short, chat_talking_prompt_short) or ""
except Exception as e:
logger.error(f"上下文黑话解释失败: {e}")
return ""
@ -762,7 +759,7 @@ class DefaultReplyer:
available_actions: Optional[Dict[str, ActionInfo]] = None,
chosen_actions: Optional[List[ActionPlannerInfo]] = None,
enable_tool: bool = True,
reply_time_point: Optional[float] = time.time(),
reply_time_point: float = time.time(),
think_level: int = 1,
unknown_words: Optional[List[str]] = None,
) -> Tuple[str, List[int], List[str], str]:
@ -1098,37 +1095,53 @@ class DefaultReplyer:
else:
reply_target_block = ""
chat_target_1 = await global_prompt_manager.get_prompt_async("chat_target_group1")
chat_target_2 = await global_prompt_manager.get_prompt_async("chat_target_group2")
chat_target_1_prompt = prompt_manager.get_prompt("chat_target_group1")
chat_target_1 = await prompt_manager.render_prompt(chat_target_1_prompt)
chat_target_2_prompt = prompt_manager.get_prompt("chat_target_group2")
chat_target_2 = await prompt_manager.render_prompt(chat_target_2_prompt)
template_name = "default_expressor_prompt"
# 根据配置构建最终的 reply_style：支持 multiple_reply_style 按概率随机替换
reply_style = global_config.personality.reply_style
multi_styles = getattr(global_config.personality, "multiple_reply_style", None) or []
multi_prob = getattr(global_config.personality, "multiple_probability", 0.0) or 0.0
multi_styles = global_config.personality.multiple_reply_style
multi_prob = global_config.personality.multiple_probability or 0.0
if multi_styles and multi_prob > 0 and random.random() < multi_prob:
try:
reply_style = random.choice(list(multi_styles))
reply_style = random.choice(multi_styles)
except Exception:
reply_style = global_config.personality.reply_style
return await global_prompt_manager.format_prompt(
template_name,
expression_habits_block=expression_habits_block,
# relation_info_block=relation_info,
chat_target=chat_target_1,
time_block=time_block,
chat_info=chat_talking_prompt_half,
identity=personality_prompt,
chat_target_2=chat_target_2,
reply_target_block=reply_target_block,
raw_reply=raw_reply,
reason=reason,
reply_style=reply_style,
keywords_reaction_prompt=keywords_reaction_prompt,
moderation_prompt=moderation_prompt_block,
)
# return await global_prompt_manager.format_prompt(
# template_name,
# expression_habits_block=expression_habits_block,
# # relation_info_block=relation_info,
# chat_target=chat_target_1,
# time_block=time_block,
# chat_info=chat_talking_prompt_half,
# identity=personality_prompt,
# chat_target_2=chat_target_2,
# reply_target_block=reply_target_block,
# raw_reply=raw_reply,
# reason=reason,
# reply_style=reply_style,
# keywords_reaction_prompt=keywords_reaction_prompt,
# moderation_prompt=moderation_prompt_block,
# )
prompt_template = prompt_manager.get_prompt("default_expressor_prompt")
prompt_template.add_context("expression_habits_block", expression_habits_block)
# prompt_template.add_context("relation_info_block", relation_info)
prompt_template.add_context("chat_target", chat_target_1)
prompt_template.add_context("time_block", time_block)
prompt_template.add_context("chat_info", chat_talking_prompt_half)
prompt_template.add_context("identity", personality_prompt)
prompt_template.add_context("chat_target_2", chat_target_2)
prompt_template.add_context("reply_target_block", reply_target_block)
prompt_template.add_context("raw_reply", raw_reply)
prompt_template.add_context("reason", reason)
prompt_template.add_context("reply_style", reply_style)
prompt_template.add_context("keywords_reaction_prompt", keywords_reaction_prompt)
prompt_template.add_context("moderation_prompt", moderation_prompt_block)
return await prompt_manager.render_prompt(prompt_template)
async def _build_single_sending_message(
self,
@ -1143,7 +1156,7 @@ class DefaultReplyer:
"""构建单个发送消息"""
bot_user_info = UserInfo(
user_id=global_config.bot.qq_account,
user_id=str(global_config.bot.qq_account),
user_nickname=global_config.bot.nickname,
platform=self.chat_stream.platform,
)
@ -1201,18 +1214,21 @@ class DefaultReplyer:
if global_config.lpmm_knowledge.lpmm_mode == "agent":
return ""
time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
bot_name = global_config.bot.nickname
prompt = await global_prompt_manager.format_prompt(
"lpmm_get_knowledge_prompt",
bot_name=bot_name,
time_now=time_now,
chat_history=message,
sender=sender,
target_message=target,
)
template_prompt = prompt_manager.get_prompt("lpmm_get_knowledge_prompt")
template_prompt.add_context("bot_name", global_config.bot.nickname)
template_prompt.add_context("time_now", lambda _: time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
template_prompt.add_context("chat_history", message)
template_prompt.add_context("sender", sender)
template_prompt.add_context("target_message", target)
# prompt = await global_prompt_manager.format_prompt(
# "lpmm_get_knowledge_prompt",
# bot_name=bot_name,
# time_now=time_now,
# chat_history=message,
# sender=sender,
# target_message=target,
# )
prompt = await prompt_manager.render_prompt(template_prompt)
_, _, _, _, tool_calls = await llm_api.generate_with_model_with_tools(
prompt,
model_config=model_config.model_task_config.tool_use,

69
src/chat/replyer/private_generator.py
View File

@ -18,6 +18,7 @@ from src.chat.message_receive.uni_message_sender import UniversalMessageSender
from src.chat.utils.timer_calculator import Timer # <--- Import Timer
from src.chat.utils.utils import get_chat_type_and_target_info, is_bot_self
from src.chat.utils.prompt_builder import global_prompt_manager
from src.prompt.prompt_manager import prompt_manager
from src.chat.utils.common_utils import TempMethodsExpression
from src.chat.utils.chat_message_builder import (
build_readable_messages,
@ -35,13 +36,11 @@ from src.plugin_system.apis import llm_api
from src.chat.replyer.prompt.lpmm_prompt import init_lpmm_prompt
from src.chat.replyer.prompt.replyer_private_prompt import init_replyer_private_prompt
from src.chat.replyer.prompt.rewrite_prompt import init_rewrite_prompt
from src.memory_system.memory_retrieval import init_memory_retrieval_prompt, build_memory_retrieval_prompt
from src.bw_learner.jargon_explainer import explain_jargon_in_context
init_lpmm_prompt()
init_replyer_private_prompt()
init_rewrite_prompt()
init_memory_retrieval_prompt()
@ -801,11 +800,11 @@ class PrivateReplyer:
# 根据配置构建最终的 reply_style：支持 multiple_reply_style 按概率随机替换
reply_style = global_config.personality.reply_style
multi_styles = getattr(global_config.personality, "multiple_reply_style", None) or []
multi_prob = getattr(global_config.personality, "multiple_probability", 0.0) or 0.0
multi_styles =global_config.personality.multiple_reply_style
multi_prob = global_config.personality.multiple_probability or 0.0
if multi_styles and multi_prob > 0 and random.random() < multi_prob:
try:
reply_style = random.choice(list(multi_styles))
reply_style = random.choice(multi_styles)
except Exception:
# 兜底：即使 multiple_reply_style 配置异常也不影响正常回复
reply_style = global_config.personality.reply_style
@ -864,7 +863,6 @@ class PrivateReplyer:
) -> str: # 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)
sender, target = self._parse_reply_target(reply_to)
target = replace_user_references(target, chat_stream.platform, replace_bot_name=True)
@ -927,10 +925,12 @@ class PrivateReplyer:
chat_target_name = "对方"
if self.chat_target_info:
chat_target_name = self.chat_target_info.person_name or self.chat_target_info.user_nickname or "对方"
chat_target_1 = await global_prompt_manager.format_prompt("chat_target_private1", sender_name=chat_target_name)
chat_target_2 = await global_prompt_manager.format_prompt("chat_target_private2", sender_name=chat_target_name)
template_name = "default_expressor_prompt"
chat_target_1_template = prompt_manager.get_prompt("chat_target_private1")
chat_target_1_template.add_context("sender_name", chat_target_name)
chat_target_1 = await prompt_manager.render_prompt(chat_target_1_template)
chat_target_2_template = prompt_manager.get_prompt("chat_target_private2")
chat_target_2_template.add_context("sender_name", chat_target_name)
chat_target_2 = await prompt_manager.render_prompt(chat_target_2_template)
# 根据配置构建最终的 reply_style：支持 multiple_reply_style 按概率随机替换
reply_style = global_config.personality.reply_style
@ -943,22 +943,37 @@ class PrivateReplyer:
# 兜底：即使 multiple_reply_style 配置异常也不影响正常回复
reply_style = global_config.personality.reply_style
return await global_prompt_manager.format_prompt(
template_name,
expression_habits_block=expression_habits_block,
# relation_info_block=relation_info,
chat_target=chat_target_1,
time_block=time_block,
chat_info=chat_talking_prompt_half,
identity=personality_prompt,
chat_target_2=chat_target_2,
reply_target_block=reply_target_block,
raw_reply=raw_reply,
reason=reason,
reply_style=reply_style,
keywords_reaction_prompt=keywords_reaction_prompt,
moderation_prompt=moderation_prompt_block,
)
# return await global_prompt_manager.format_prompt(
# template_name,
# expression_habits_block=expression_habits_block,
# # relation_info_block=relation_info,
# chat_target=chat_target_1,
# time_block=time_block,
# chat_info=chat_talking_prompt_half,
# identity=personality_prompt,
# chat_target_2=chat_target_2,
# reply_target_block=reply_target_block,
# raw_reply=raw_reply,
# reason=reason,
# reply_style=reply_style,
# keywords_reaction_prompt=keywords_reaction_prompt,
# moderation_prompt=moderation_prompt_block,
# )
prompt_template = prompt_manager.get_prompt("default_expressor_prompt")
prompt_template.add_context("expression_habits_block", expression_habits_block)
# prompt_template.add_context("relation_info_block", relation_info)
prompt_template.add_context("chat_target", chat_target_1)
prompt_template.add_context("time_block", time_block)
prompt_template.add_context("chat_info", chat_talking_prompt_half)
prompt_template.add_context("identity", personality_prompt)
prompt_template.add_context("chat_target_2", chat_target_2)
prompt_template.add_context("reply_target_block", reply_target_block)
prompt_template.add_context("raw_reply", raw_reply)
prompt_template.add_context("reason", reason)
prompt_template.add_context("reply_style", reply_style)
prompt_template.add_context("keywords_reaction_prompt", keywords_reaction_prompt)
prompt_template.add_context("moderation_prompt", moderation_prompt_block)
return await prompt_manager.render_prompt(prompt_template)
async def _build_single_sending_message(
self,
@ -973,7 +988,7 @@ class PrivateReplyer:
"""构建单个发送消息"""
bot_user_info = UserInfo(
user_id=global_config.bot.qq_account,
user_id=str(global_config.bot.qq_account),
user_nickname=global_config.bot.nickname,
platform=self.chat_stream.platform,
)