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MaiBot
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Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev

pull/1217/head
SengokuCola 2025-08-22 23:49:26 +08:00
parent 0bbd9a4e8d 2bb4ec4ae2
commit add05cee55
7 changed files with 93 additions and 92 deletions
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8
src/chat/heart_flow/heartFC_chat.py
View File

@ -718,7 +718,7 @@ class HeartFChatting:
}
else:
try:
success, response_set, prompt, selected_expressions = await generator_api.generate_reply(
success, llm_response = await generator_api.generate_reply(
chat_stream=self.chat_stream,
reply_message=action_planner_info.action_message,
available_actions=available_actions,
@ -727,10 +727,9 @@ class HeartFChatting:
enable_tool=global_config.tool.enable_tool,
request_type="replyer",
from_plugin=False,
return_expressions=True,
)
if not success or not response_set:
if not success or not llm_response or not llm_response.reply_set:
if action_planner_info.action_message:
logger.info(f"对 {action_planner_info.action_message.processed_plain_text} 的回复生成失败")
else:
@ -740,7 +739,8 @@ class HeartFChatting:
except asyncio.CancelledError:
logger.debug(f"{self.log_prefix} 并行执行：回复生成任务已被取消")
return {"action_type": "reply", "success": False, "reply_text": "", "loop_info": None}
response_set = llm_response.reply_set
selected_expressions = llm_response.selected_expressions
loop_info, reply_text, _ = await self._send_and_store_reply(
response_set=response_set,
action_message=action_planner_info.action_message, # type: ignore

8
src/chat/planner_actions/action_modifier.py
View File

@ -2,7 +2,7 @@ import random
import asyncio
import hashlib
import time
from typing import List, Any, Dict, TYPE_CHECKING, Tuple
from typing import List, Dict, TYPE_CHECKING, Tuple
from src.common.logger import get_logger
from src.config.config import global_config, model_config
@ -163,7 +163,7 @@ class ActionModifier:
deactivated_actions = []
# 分类处理不同激活类型的actions
llm_judge_actions = {}
llm_judge_actions: Dict[str, ActionInfo] = {}
actions_to_check = list(actions_with_info.items())
random.shuffle(actions_to_check)
@ -220,7 +220,7 @@ class ActionModifier:
async def _process_llm_judge_actions_parallel(
self,
llm_judge_actions: Dict[str, Any],
llm_judge_actions: Dict[str, ActionInfo],
chat_content: str = "",
) -> Dict[str, bool]:
"""
@ -239,7 +239,7 @@ class ActionModifier:
current_time = time.time()
results = {}
tasks_to_run = {}
tasks_to_run: Dict[str, ActionInfo] = {}
# 检查缓存
for action_name, action_info in llm_judge_actions.items():

63
src/chat/replyer/default_generator.py
View File

@ -10,6 +10,7 @@ from src.mais4u.mai_think import mai_thinking_manager
from src.common.logger import get_logger
from src.common.data_models.database_data_model import DatabaseMessages
from src.common.data_models.info_data_model import ActionPlannerInfo
from src.common.data_models.llm_data_model import LLMGenerationDataModel
from src.config.config import global_config, model_config
from src.llm_models.utils_model import LLMRequest
from src.chat.message_receive.message import UserInfo, Seg, MessageRecv, MessageSending
@ -162,7 +163,7 @@ class DefaultReplyer:
from_plugin: bool = True,
stream_id: Optional[str] = None,
reply_message: Optional[DatabaseMessages] = None,
) -> Tuple[bool, Optional[Dict[str, Any]], Optional[str], Optional[List[int]]]:
) -> Tuple[bool, LLMGenerationDataModel]:
# sourcery skip: merge-nested-ifs
"""
回复器 (Replier): 负责生成回复文本的核心逻辑。
@ -182,6 +183,7 @@ class DefaultReplyer:
prompt = None
selected_expressions: Optional[List[int]] = None
llm_response = LLMGenerationDataModel()
if available_actions is None:
available_actions = {}
try:
@ -195,10 +197,12 @@ class DefaultReplyer:
reply_message=reply_message,
reply_reason=reply_reason,
)
llm_response.prompt = prompt
llm_response.selected_expressions = selected_expressions
if not prompt:
logger.warning("构建prompt失败，跳过回复生成")
return False, None, None, []
return False, llm_response
from src.plugin_system.core.events_manager import events_manager
if not from_plugin:
@ -215,12 +219,10 @@ class DefaultReplyer:
try:
content, reasoning_content, model_name, tool_call = await self.llm_generate_content(prompt)
logger.debug(f"replyer生成内容: {content}")
llm_response = {
"content": content,
"reasoning": reasoning_content,
"model": model_name,
"tool_calls": tool_call,
}
llm_response.content = content
llm_response.reasoning = reasoning_content
llm_response.model = model_name
llm_response.tool_calls = tool_call
if not from_plugin and not await events_manager.handle_mai_events(
EventType.AFTER_LLM, None, prompt, llm_response, stream_id=stream_id
):
@ -230,24 +232,23 @@ 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, selected_expressions # LLM 调用失败则无法生成回复
return False, llm_response # LLM 调用失败则无法生成回复
return True, llm_response, prompt, selected_expressions
return True, llm_response
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, selected_expressions
return False, llm_response
async def rewrite_reply_with_context(
self,
raw_reply: str = "",
reason: str = "",
reply_to: str = "",
return_prompt: bool = False,
) -> Tuple[bool, Optional[str], Optional[str]]:
) -> Tuple[bool, LLMGenerationDataModel]:
"""
表达器 (Expressor): 负责重写和优化回复文本。
@ -260,6 +261,7 @@ class DefaultReplyer:
Returns:
Tuple[bool, Optional[str]]: (是否成功, 重写后的回复内容)
"""
llm_response = LLMGenerationDataModel()
try:
with Timer("构建Prompt", {}): # 内部计时器，可选保留
prompt = await self.build_prompt_rewrite_context(
@ -267,29 +269,33 @@ class DefaultReplyer:
reason=reason,
reply_to=reply_to,
)
llm_response.prompt = prompt
content = None
reasoning_content = None
model_name = "unknown_model"
if not prompt:
logger.error("Prompt 构建失败，无法生成回复。")
return False, None, None
return False, llm_response
try:
content, reasoning_content, model_name, _ = await self.llm_generate_content(prompt)
logger.info(f"想要表达：{raw_reply}||理由：{reason}||生成回复: {content}\n")
llm_response.content = content
llm_response.reasoning = reasoning_content
llm_response.model = model_name
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 if return_prompt else None # LLM 调用失败则无法生成回复
return False, llm_response # LLM 调用失败则无法生成回复
return True, content, prompt if return_prompt else None
return True, llm_response
except Exception as e:
logger.error(f"回复生成意外失败: {e}")
traceback.print_exc()
return False, None, prompt if return_prompt else None
return False, llm_response
async def build_relation_info(self, sender: str, target: str):
if not global_config.relationship.enable_relationship:
@ -375,9 +381,7 @@ class DefaultReplyer:
if global_config.memory.enable_instant_memory:
chat_history_str = build_readable_messages(
messages=chat_history,
replace_bot_name=True,
timestamp_mode="normal"
messages=chat_history, replace_bot_name=True, timestamp_mode="normal"
)
asyncio.create_task(self.instant_memory.create_and_store_memory(chat_history_str))
@ -668,16 +672,18 @@ class DefaultReplyer:
action_descriptions += chosen_action_descriptions
return action_descriptions
async def build_personality_prompt(self) -> str:
bot_name = global_config.bot.nickname
if global_config.bot.alias_names:
bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}"
else:
bot_nickname = ""
prompt_personality = f"{global_config.personality.personality_core};{global_config.personality.personality_side}"
return f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}"
prompt_personality = (
f"{global_config.personality.personality_core};{global_config.personality.personality_side}"
)
return f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}"
async def build_prompt_reply_context(
self,
@ -875,17 +881,12 @@ class DefaultReplyer:
raw_reply: str,
reason: str,
reply_to: str,
reply_message: Optional[Dict[str, Any]] = None,
) -> 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)
if reply_message:
sender = reply_message.get("sender", "")
target = reply_message.get("target", "")
else:
sender, target = self._parse_reply_target(reply_to)
sender, target = self._parse_reply_target(reply_to)
# 添加情绪状态获取
if global_config.mood.enable_mood:
@ -908,7 +909,7 @@ class DefaultReplyer:
)
# 并行执行2个构建任务
(expression_habits_block, _), relation_info, personality_prompt = await asyncio.gather(
(expression_habits_block, _), relation_info, personality_prompt = await asyncio.gather(
self.build_expression_habits(chat_talking_prompt_half, target),
self.build_relation_info(sender, target),
self.build_personality_prompt(),

16
src/common/data_models/llm_data_model.py 100644
View File

@ -0,0 +1,16 @@
from dataclasses import dataclass
from typing import Optional, List, Tuple, TYPE_CHECKING, Any
from . import BaseDataModel
if TYPE_CHECKING:
from src.llm_models.payload_content.tool_option import ToolCall
@dataclass
class LLMGenerationDataModel(BaseDataModel):
content: Optional[str] = None
reasoning: Optional[str] = None
model: Optional[str] = None
tool_calls: Optional[List["ToolCall"]] = None
prompt: Optional[str] = None
selected_expressions: Optional[List[int]] = None
reply_set: Optional[List[Tuple[str, Any]]] = None

53
src/plugin_system/apis/generator_api.py
View File

@ -21,6 +21,7 @@ from src.plugin_system.base.component_types import ActionInfo
if TYPE_CHECKING:
from src.common.data_models.info_data_model import ActionPlannerInfo
from src.common.data_models.database_data_model import DatabaseMessages
from src.common.data_models.llm_data_model import LLMGenerationDataModel
install(extra_lines=3)
@ -85,11 +86,9 @@ async def generate_reply(
enable_tool: bool = False,
enable_splitter: bool = True,
enable_chinese_typo: bool = True,
return_prompt: bool = False,
request_type: str = "generator_api",
from_plugin: bool = True,
return_expressions: bool = False,
) -> Tuple[bool, List[Tuple[str, Any]], Optional[str], Optional[List[int]]]:
) -> Tuple[bool, Optional["LLMGenerationDataModel"]]:
"""生成回复
Args:
@ -117,7 +116,7 @@ async def generate_reply(
replyer = get_replyer(chat_stream, chat_id, request_type=request_type)
if not replyer:
logger.error("[GeneratorAPI] 无法获取回复器")
return False, [], None, None
return False, None
if not extra_info and action_data:
extra_info = action_data.get("extra_info", "")
@ -126,7 +125,7 @@ async def generate_reply(
reply_reason = action_data.get("reason", "")
# 调用回复器生成回复
success, llm_response_dict, prompt, selected_expressions = await replyer.generate_reply_with_context(
success, llm_response = await replyer.generate_reply_with_context(
extra_info=extra_info,
available_actions=available_actions,
chosen_actions=chosen_actions,
@ -138,43 +137,27 @@ async def generate_reply(
)
if not success:
logger.warning("[GeneratorAPI] 回复生成失败")
return False, [], None, None
assert llm_response_dict is not None, "llm_response_dict不应为None" # 虽然说不会出现llm_response为空的情况
if content := llm_response_dict.get("content", ""):
return False, None
if content := llm_response.content:
reply_set = process_human_text(content, enable_splitter, enable_chinese_typo)
else:
reply_set = []
llm_response.reply_set = reply_set
logger.debug(f"[GeneratorAPI] 回复生成成功，生成了 {len(reply_set)} 个回复项")
# if return_prompt:
# if return_expressions:
# return success, reply_set, prompt, selected_expressions
# else:
# return success, reply_set, prompt, None
# else:
# if return_expressions:
# return success, reply_set, (None, selected_expressions)
# else:
# return success, reply_set, None
return (
success,
reply_set,
prompt if return_prompt else None,
selected_expressions if return_expressions else None,
)
return success, llm_response
except ValueError as ve:
raise ve
except UserWarning as uw:
logger.warning(f"[GeneratorAPI] 中断了生成: {uw}")
return False, [], None, None
return False, None
except Exception as e:
logger.error(f"[GeneratorAPI] 生成回复时出错: {e}")
logger.error(traceback.format_exc())
return False, [], None, None
return False, None
async def rewrite_reply(
chat_stream: Optional[ChatStream] = None,
@ -185,9 +168,8 @@ async def rewrite_reply(
raw_reply: str = "",
reason: str = "",
reply_to: str = "",
return_prompt: bool = False,
request_type: str = "generator_api",
) -> Tuple[bool, List[Tuple[str, Any]], Optional[str]]:
) -> Tuple[bool, Optional["LLMGenerationDataModel"]]:
"""重写回复
Args:
@ -210,7 +192,7 @@ async def rewrite_reply(
replyer = get_replyer(chat_stream, chat_id, request_type=request_type)
if not replyer:
logger.error("[GeneratorAPI] 无法获取回复器")
return False, [], None
return False, None
logger.info("[GeneratorAPI] 开始重写回复")
@ -221,29 +203,28 @@ async def rewrite_reply(
reply_to = reply_to or reply_data.get("reply_to", "")
# 调用回复器重写回复
success, content, prompt = await replyer.rewrite_reply_with_context(
success, llm_response = await replyer.rewrite_reply_with_context(
raw_reply=raw_reply,
reason=reason,
reply_to=reply_to,
return_prompt=return_prompt,
)
reply_set = []
if content:
if success and llm_response and (content := llm_response.content):
reply_set = process_human_text(content, enable_splitter, enable_chinese_typo)
llm_response.reply_set = reply_set
if success:
logger.info(f"[GeneratorAPI] 重写回复成功，生成了 {len(reply_set)} 个回复项")
else:
logger.warning("[GeneratorAPI] 重写回复失败")
return success, reply_set, prompt if return_prompt else None
return success, llm_response
except ValueError as ve:
raise ve
except Exception as e:
logger.error(f"[GeneratorAPI] 重写回复时出错: {e}")
return False, [], None
return False, None
def process_human_text(content: str, enable_splitter: bool, enable_chinese_typo: bool) -> List[Tuple[str, Any]]:

31
src/plugin_system/core/events_manager.py
View File

@ -1,6 +1,6 @@
import asyncio
import contextlib
from typing import List, Dict, Optional, Type, Tuple, Any, Coroutine
from typing import List, Dict, Optional, Type, Tuple, Any, TYPE_CHECKING
from src.chat.message_receive.message import MessageRecv
from src.chat.message_receive.chat_stream import get_chat_manager
@ -9,6 +9,9 @@ from src.plugin_system.base.component_types import EventType, EventHandlerInfo,
from src.plugin_system.base.base_events_handler import BaseEventHandler
from .global_announcement_manager import global_announcement_manager
if TYPE_CHECKING:
from src.common.data_models.llm_data_model import LLMGenerationDataModel
logger = get_logger("events_manager")
@ -47,7 +50,7 @@ class EventsManager:
event_type: EventType,
message: Optional[MessageRecv] = None,
llm_prompt: Optional[str] = None,
llm_response: Optional[Dict[str, Any]] = None,
llm_response: Optional["LLMGenerationDataModel"] = None,
stream_id: Optional[str] = None,
action_usage: Optional[List[str]] = None,
) -> Optional[MaiMessages]:
@ -97,7 +100,7 @@ class EventsManager:
event_type: EventType,
message: Optional[MessageRecv] = None,
llm_prompt: Optional[str] = None,
llm_response: Optional[Dict[str, Any]] = None,
llm_response: Optional["LLMGenerationDataModel"] = None,
stream_id: Optional[str] = None,
action_usage: Optional[List[str]] = None,
) -> bool:
@ -175,16 +178,16 @@ class EventsManager:
return False
def _transform_event_message(
self, message: MessageRecv, llm_prompt: Optional[str] = None, llm_response: Optional[Dict[str, Any]] = None
self, message: MessageRecv, llm_prompt: Optional[str] = None, llm_response: Optional["LLMGenerationDataModel"] = None
) -> MaiMessages:
"""转换事件消息格式"""
# 直接赋值部分内容
transformed_message = MaiMessages(
llm_prompt=llm_prompt,
llm_response_content=llm_response.get("content") if llm_response else None,
llm_response_reasoning=llm_response.get("reasoning") if llm_response else None,
llm_response_model=llm_response.get("model") if llm_response else None,
llm_response_tool_call=llm_response.get("tool_calls") if llm_response else None,
llm_response_content=llm_response.content if llm_response else None,
llm_response_reasoning=llm_response.reasoning if llm_response else None,
llm_response_model=llm_response.model if llm_response else None,
llm_response_tool_call=llm_response.tool_calls if llm_response else None,
raw_message=message.raw_message,
additional_data=message.message_info.additional_config or {},
)
@ -228,7 +231,7 @@ class EventsManager:
return transformed_message
def _build_message_from_stream(
self, stream_id: str, llm_prompt: Optional[str] = None, llm_response: Optional[Dict[str, Any]] = None
self, stream_id: str, llm_prompt: Optional[str] = None, llm_response: Optional["LLMGenerationDataModel"] = None
) -> MaiMessages:
"""从流ID构建消息"""
chat_stream = get_chat_manager().get_stream(stream_id)
@ -240,7 +243,7 @@ class EventsManager:
self,
stream_id: str,
llm_prompt: Optional[str] = None,
llm_response: Optional[Dict[str, Any]] = None,
llm_response: Optional["LLMGenerationDataModel"] = None,
action_usage: Optional[List[str]] = None,
) -> MaiMessages:
"""没有message对象时进行转换"""
@ -249,10 +252,10 @@ class EventsManager:
return MaiMessages(
stream_id=stream_id,
llm_prompt=llm_prompt,
llm_response_content=(llm_response.get("content") if llm_response else None),
llm_response_reasoning=(llm_response.get("reasoning") if llm_response else None),
llm_response_model=llm_response.get("model") if llm_response else None,
llm_response_tool_call=(llm_response.get("tool_calls") if llm_response else None),
llm_response_content=(llm_response.content if llm_response else None),
llm_response_reasoning=(llm_response.reasoning if llm_response else None),
llm_response_model=(llm_response.model if llm_response else None),
llm_response_tool_call=(llm_response.tool_calls if llm_response else None),
is_group_message=(not (not chat_stream.group_info)),
is_private_message=(not chat_stream.group_info),
action_usage=action_usage,

6
template/model_config_template.toml
View File

@ -1,5 +1,5 @@
[inner]
version = "1.4.0"
version = "1.4.1"
# 配置文件版本号迭代规则同bot_config.toml
@ -40,14 +40,14 @@ price_out = 8.0 # 输出价格（用于API调用统计，单
#force_stream_mode = true # 强制流式输出模式（若模型不支持非流式输出，请取消该注释，启用强制流式输出，若无该字段，默认值为false）
[[models]]
model_identifier = "Pro/deepseek-ai/DeepSeek-V3"
model_identifier = "deepseek-ai/DeepSeek-V3"
name = "siliconflow-deepseek-v3"
api_provider = "SiliconFlow"
price_in = 2.0
price_out = 8.0
[[models]]
model_identifier = "Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
model_identifier = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
name = "deepseek-r1-distill-qwen-32b"
api_provider = "SiliconFlow"
price_in = 4.0