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部分模块的新数据结构适配

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This commit is contained in:
DrSmoothl
2026-03-13 23:36:17 +08:00
parent 6201b862c9
commit 898fab6de9
7 changed files with 580 additions and 399 deletions
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11
src/chat/brain_chat/brain_chat.py
View File

@@ -8,7 +8,7 @@ from rich.traceback import install
from src.config.config import global_config
from src.common.logger import get_logger
from src.common.data_models.info_data_model import ActionPlannerInfo
from src.common.data_models.message_data_model import ReplyContentType
from src.common.data_models.message_component_data_model import MessageSequence, TextComponent
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
from src.chat.utils.prompt_builder import global_prompt_manager
from src.chat.utils.timer_calculator import Timer
@@ -35,7 +35,6 @@ from src.chat.utils.chat_message_builder import (
if TYPE_CHECKING:
from src.common.data_models.database_data_model import DatabaseMessages
from src.common.data_models.message_data_model import ReplySetModel
ERROR_LOOP_INFO = {
@@ -513,7 +512,7 @@ class BrainChatting:
async def _send_response(
self,
reply_set: "ReplySetModel",
reply_set: MessageSequence,
message_data: "DatabaseMessages",
selected_expressions: Optional[List[int]] = None,
) -> str:
@@ -528,10 +527,10 @@ class BrainChatting:
reply_text = ""
first_replied = False
for reply_content in reply_set.reply_data:
if reply_content.content_type != ReplyContentType.TEXT:
for component in reply_set.components:
if not isinstance(component, TextComponent):
continue
data: str = reply_content.content # type: ignore
data = component.text
if not first_replied:
await send_api.text_to_stream(
text=data,

105
src/chat/replyer/group_generator.py
View File

@@ -7,29 +7,31 @@ import re
from typing import List, Optional, Dict, Any, Tuple
from datetime import datetime
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_old import UserInfo, Seg, MessageRecv, MessageSending
from src.chat.message_receive.chat_stream import ChatStream
from maim_message import BaseMessageInfo, MessageBase, Seg
from src.common.data_models.mai_message_data_model import MaiMessage, UserInfo
from src.chat.message_receive.message import SessionMessage
from src.chat.message_receive.chat_manager import BotChatSession
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.chat_message_builder import (
from src.services.message_service import (
build_readable_messages,
get_raw_msg_before_timestamp_with_chat,
replace_user_references,
translate_pid_to_description,
)
from src.bw_learner.expression_selector import expression_selector
from src.plugin_system.apis.message_api import translate_pid_to_description
# from src.memory_system.memory_activator import MemoryActivator
from src.person_info.person_info import Person
from src.plugin_system.base.component_types import ActionInfo, EventType
from src.plugin_system.apis import llm_api
from src.core.types import ActionInfo, EventType
from src.services import llm_service as llm_api
from src.chat.logger.plan_reply_logger import PlanReplyLogger
from src.memory_system.memory_retrieval import init_memory_retrieval_sys, build_memory_retrieval_prompt
@@ -45,17 +47,17 @@ logger = get_logger("replyer")
class DefaultReplyer:
def __init__(
self,
chat_stream: ChatStream,
chat_stream: BotChatSession,
request_type: str = "replyer",
):
self.express_model = LLMRequest(model_set=model_config.model_task_config.replyer, request_type=request_type)
self.chat_stream = chat_stream
self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.stream_id)
self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.session_id)
self.heart_fc_sender = UniversalMessageSender()
from src.plugin_system.core.tool_use import ToolExecutor # 延迟导入ToolExecutor，不然会循环依赖
from src.chat.tool_executor import ToolExecutor
self.tool_executor = ToolExecutor(chat_id=self.chat_stream.stream_id, enable_cache=True, cache_ttl=3)
self.tool_executor = ToolExecutor(chat_id=self.chat_stream.session_id, enable_cache=True, cache_ttl=3)
async def generate_reply_with_context(
self,
@@ -66,7 +68,7 @@ class DefaultReplyer:
enable_tool: bool = True,
from_plugin: bool = True,
stream_id: Optional[str] = None,
reply_message: Optional[DatabaseMessages] = None,
reply_message: Optional[SessionMessage] = None,
reply_time_point: float = time.time(),
think_level: int = 1,
unknown_words: Optional[List[str]] = None,
@@ -132,7 +134,7 @@ class DefaultReplyer:
if log_reply:
try:
PlanReplyLogger.log_reply(
chat_id=self.chat_stream.stream_id,
chat_id=self.chat_stream.session_id,
prompt="",
output=None,
processed_output=None,
@@ -146,12 +148,12 @@ class DefaultReplyer:
except Exception:
logger.exception("记录reply日志失败")
return False, llm_response
from src.plugin_system.core.events_manager import events_manager
from src.core.event_bus import event_bus
from src.chat.event_helpers import build_event_message
if not from_plugin:
continue_flag, modified_message = await events_manager.handle_mai_events(
EventType.POST_LLM, None, prompt, None, stream_id=stream_id
)
_event_msg = build_event_message(EventType.POST_LLM, llm_prompt=prompt, stream_id=stream_id)
continue_flag, modified_message = await event_bus.emit(EventType.POST_LLM, _event_msg)
if not continue_flag:
raise UserWarning("插件于请求前中断了内容生成")
if modified_message and modified_message._modify_flags.modify_llm_prompt:
@@ -202,7 +204,7 @@ class DefaultReplyer:
try:
if log_reply:
PlanReplyLogger.log_reply(
chat_id=self.chat_stream.stream_id,
chat_id=self.chat_stream.session_id,
prompt=prompt,
output=content,
processed_output=None,
@@ -214,9 +216,10 @@ class DefaultReplyer:
)
except Exception:
logger.exception("记录reply日志失败")
continue_flag, modified_message = await events_manager.handle_mai_events(
EventType.AFTER_LLM, None, prompt, llm_response, stream_id=stream_id
_event_msg = build_event_message(
EventType.AFTER_LLM, llm_prompt=prompt, llm_response=llm_response, stream_id=stream_id
)
continue_flag, modified_message = await event_bus.emit(EventType.AFTER_LLM, _event_msg)
if not from_plugin and not continue_flag:
raise UserWarning("插件于请求后取消了内容生成")
if modified_message:
@@ -259,7 +262,7 @@ class DefaultReplyer:
if log_reply:
try:
PlanReplyLogger.log_reply(
chat_id=self.chat_stream.stream_id,
chat_id=self.chat_stream.session_id,
prompt=prompt or "",
output=None,
processed_output=None,
@@ -353,14 +356,14 @@ class DefaultReplyer:
str: 表达习惯信息字符串
"""
# 检查是否允许在此聊天流中使用表达
use_expression, _, _ = TempMethodsExpression.get_expression_config_for_chat(self.chat_stream.stream_id)
use_expression, _, _ = TempMethodsExpression.get_expression_config_for_chat(self.chat_stream.session_id)
if not use_expression:
return "", []
style_habits = []
# 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,
self.chat_stream.session_id,
chat_history,
max_num=8,
target_message=target,
@@ -594,7 +597,7 @@ class DefaultReplyer:
async def _build_jargon_explanation(
self,
chat_id: str,
messages_short: List[DatabaseMessages],
messages_short: List[SessionMessage],
chat_talking_prompt_short: str,
unknown_words: Optional[List[str]],
) -> str:
@@ -703,9 +706,13 @@ class DefaultReplyer:
is_group = stream_type == "group"
# 使用 ChatManager 提供的接口生成 chat_id，避免在此重复实现逻辑
from src.chat.message_receive.chat_stream import get_chat_manager
from src.common.utils.utils_session import SessionUtils
chat_id = get_chat_manager().get_stream_id(platform, str(id_str), is_group=is_group)
chat_id = SessionUtils.calculate_session_id(
platform,
group_id=str(id_str) if is_group else None,
user_id=str(id_str) if not is_group else None,
)
return chat_id, prompt_content
except (ValueError, IndexError):
@@ -751,7 +758,7 @@ class DefaultReplyer:
async def build_prompt_reply_context(
self,
reply_message: Optional[DatabaseMessages] = None,
reply_message: Optional[SessionMessage] = None,
extra_info: str = "",
reply_reason: str = "",
available_actions: Optional[Dict[str, ActionInfo]] = None,
@@ -778,7 +785,7 @@ class DefaultReplyer:
if available_actions is None:
available_actions = {}
chat_stream = self.chat_stream
chat_id = chat_stream.stream_id
chat_id = chat_stream.session_id
_is_group_chat = bool(chat_stream.group_info)
platform = chat_stream.platform
@@ -1005,7 +1012,7 @@ class DefaultReplyer:
reply_to: str,
) -> str: # sourcery skip: merge-else-if-into-elif, remove-redundant-if
chat_stream = self.chat_stream
chat_id = chat_stream.stream_id
chat_id = chat_stream.session_id
sender, target = self._parse_reply_target(reply_to)
target = replace_user_references(target, chat_stream.platform, replace_bot_name=True)
@@ -1105,31 +1112,29 @@ class DefaultReplyer:
is_emoji: bool,
thinking_start_time: float,
display_message: str,
anchor_message: Optional[MessageRecv] = None,
) -> MessageSending:
anchor_message: Optional[MaiMessage] = None,
) -> SessionMessage:
"""构建单个发送消息"""
bot_user_info = UserInfo(
user_id=str(global_config.bot.qq_account),
user_nickname=global_config.bot.nickname,
platform=self.chat_stream.platform,
)
# await anchor_message.process()
sender_info = anchor_message.message_info.user_info if anchor_message else None
return MessageSending(
message_id=message_id, # 使用片段的唯一ID
chat_stream=self.chat_stream,
bot_user_info=bot_user_info,
sender_info=sender_info,
maim_message = MessageBase(
message_info=BaseMessageInfo(
platform=self.chat_stream.platform,
message_id=message_id,
time=thinking_start_time,
user_info=UserInfo(
user_id=str(global_config.bot.qq_account),
user_nickname=global_config.bot.nickname,
),
additional_config={},
),
message_segment=message_segment,
reply=anchor_message, # 回复原始锚点
is_head=reply_to,
is_emoji=is_emoji,
thinking_start_time=thinking_start_time, # 传递原始思考开始时间
display_message=display_message,
)
message = SessionMessage.from_maim_message(maim_message)
message.session_id = self.chat_stream.session_id
message.display_message = display_message
message.reply_to = anchor_message.message_id if reply_to and anchor_message else None
message.is_emoji = is_emoji
return message
async def llm_generate_content(self, prompt: str):
with Timer("LLM生成", {}): # 内部计时器，可选保留

103
src/chat/replyer/private_generator.py
View File

@@ -7,33 +7,31 @@ import re
from typing import List, Optional, Dict, Any, Tuple
from datetime import datetime
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 maim_message import Seg
from maim_message import BaseMessageInfo, MessageBase, Seg
from src.common.data_models.mai_message_data_model import MaiMessage, UserInfo
from src.chat.message_receive.message_old import MessageSending
from src.chat.message_receive.message import SessionMessage
from src.chat.message_receive.chat_manager import BotChatSession
from src.chat.message_receive.uni_message_sender import UniversalMessageSender
from src.chat.utils.timer_calculator 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.common_utils import TempMethodsExpression
from src.chat.utils.chat_message_builder import (
from src.services.message_service import (
build_readable_messages,
get_raw_msg_before_timestamp_with_chat,
replace_user_references,
translate_pid_to_description,
)
from src.bw_learner.expression_selector import expression_selector
from src.services.message_service import translate_pid_to_description
# from src.memory_system.memory_activator import MemoryActivator
from src.person_info.person_info import Person, is_person_known
from src.core.types import ActionInfo, EventType
from src.services import llm_service as llm_api
from src.memory_system.memory_retrieval import init_memory_retrieval_sys, build_memory_retrieval_prompt
from src.bw_learner.jargon_explainer_old import explain_jargon_in_context
@@ -69,7 +67,7 @@ class PrivateReplyer:
from_plugin: bool = True,
think_level: int = 1,
stream_id: Optional[str] = None,
reply_message: Optional[DatabaseMessages] = None,
reply_message: Optional[SessionMessage] = None,
reply_time_point: Optional[float] = time.time(),
unknown_words: Optional[List[str]] = None,
log_reply: bool = True,
@@ -604,7 +602,7 @@ class PrivateReplyer:
async def build_prompt_reply_context(
self,
reply_message: Optional[DatabaseMessages] = None,
reply_message: Optional[SessionMessage] = None,
extra_info: str = "",
reply_reason: str = "",
available_actions: Optional[Dict[str, ActionInfo]] = None,
@@ -954,28 +952,29 @@ class PrivateReplyer:
thinking_start_time: float,
display_message: str,
anchor_message: Optional[MaiMessage] = None,
) -> MessageSending:
) -> SessionMessage:
"""构建单个发送消息"""
bot_user_info = UserInfo(
user_id=str(global_config.bot.qq_account),
user_nickname=global_config.bot.nickname,
)
sender_info = anchor_message.message_info.user_info if anchor_message else None
return MessageSending(
message_id=message_id,
session=self.chat_stream,
bot_user_info=bot_user_info,
sender_info=sender_info,
maim_message = MessageBase(
message_info=BaseMessageInfo(
platform=self.chat_stream.platform,
message_id=message_id,
time=thinking_start_time,
user_info=UserInfo(
user_id=str(global_config.bot.qq_account),
user_nickname=global_config.bot.nickname,
),
group_info=None,
additional_config={},
),
message_segment=message_segment,
reply=anchor_message,
is_head=reply_to,
is_emoji=is_emoji,
thinking_start_time=thinking_start_time,
display_message=display_message,
)
message = SessionMessage.from_maim_message(maim_message)
message.session_id = self.chat_stream.session_id
message.display_message = display_message
message.reply_to = anchor_message.message_id if reply_to and anchor_message else None
message.is_emoji = is_emoji
return message
async def llm_generate_content(self, prompt: str):
with Timer("LLM生成", {}): # 内部计时器，可选保留
@@ -999,55 +998,9 @@ class PrivateReplyer:
return content, reasoning_content, model_name, tool_calls
async def get_prompt_info(self, message: str, sender: str, target: str):
related_info = ""
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:
# 检查LPMM知识库是否启用
if not global_config.lpmm_knowledge.enable:
logger.debug("LPMM知识库未启用，跳过获取知识库内容")
return ""
if global_config.lpmm_knowledge.lpmm_mode == "agent":
return ""
prompt_template = prompt_manager.get_prompt("lpmm_get_knowledge")
prompt_template.add_context("bot_name", global_config.bot.nickname)
prompt_template.add_context("time_now", time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
prompt_template.add_context("chat_history", message)
prompt_template.add_context("sender", sender)
prompt_template.add_context("target_message", target)
prompt = await prompt_manager.render_prompt(prompt_template)
_, _, _, _, tool_calls = await llm_api.generate_with_model_with_tools(
prompt,
model_config=model_config.model_task_config.tool_use,
tool_options=[SearchKnowledgeFromLPMMTool.get_tool_definition()],
)
if tool_calls:
result = await self.tool_executor.execute_tool_call(tool_calls[0], SearchKnowledgeFromLPMMTool())
end_time = time.time()
if not result or not result.get("content"):
logger.debug("从LPMM知识库获取知识失败，返回空知识...")
return ""
found_knowledge_from_lpmm = result.get("content", "")
logger.debug(
f"从LPMM知识库获取知识，相关信息：{found_knowledge_from_lpmm[:100]}...，信息长度: {len(found_knowledge_from_lpmm)}"
)
related_info += found_knowledge_from_lpmm
logger.debug(f"获取知识库内容耗时: {(end_time - start_time):.3f}秒")
logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
return f"你有以下这些**知识**：\n{related_info}\n请你**记住上面的知识**，之后可能会用到。\n"
else:
logger.debug("模型认为不需要使用LPMM知识库")
return ""
except Exception as e:
logger.error(f"获取知识库内容时发生异常: {str(e)}")
return ""
logger.debug(f"已跳过知识库信息获取，元消息：{message[:30]}...，消息长度: {len(message)}")
del message, sender, target
return ""
def weighted_sample_no_replacement(items, weights, k) -> list: