Losita/mai-bot
0
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

更改generator的返回值为一个数据模型稳定api

Browse Source
This commit is contained in:
UnCLAS-Prommer
2025-08-22 23:40:24 +08:00
parent 2d4fd08ac5
commit 1eeabe76ba
6 changed files with 90 additions and 89 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
src/chat/heart_flow/heartFC_chat.py
View File

@@ -679,7 +679,7 @@ 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,
@@ -688,10 +688,9 @@ 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:
@@ -701,7 +700,8 @@ 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
@@ -161,7 +161,7 @@ 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)
@@ -218,7 +218,7 @@ 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]:
"""
@@ -237,7 +237,7 @@ 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(),