更改generator的返回值为一个数据模型稳定api

src/chat/heart_flow/heartFC_chat.py

@@ -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

src/chat/planner_actions/action_modifier.py

@@ -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():

src/chat/replyer/default_generator.py

@@ -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 = {
+                llm_response.content = content
-                    "content": content,
+                llm_response.reasoning = reasoning_content
-                    "reasoning": reasoning_content,
+                llm_response.model = model_name
-                    "model": model_name,
+                llm_response.tool_calls = tool_call
-                    "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, LLMGenerationDataModel]:
-    ) -> Tuple[bool, Optional[str], Optional[str]]:
         """
         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,
+                messages=chat_history, replace_bot_name=True, timestamp_mode="normal"
-                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}"
+        prompt_personality = (
-        return f"你的名字是{bot_name}{bot_nickname}，你{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, target = self._parse_reply_target(reply_to)
-            sender = reply_message.get("sender", "")
-            target = reply_message.get("target", "")
-        else:
-            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(),

src/common/data_models/llm_data_model.py

@@ -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

src/plugin_system/apis/generator_api.py

@@ -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, Optional["LLMGenerationDataModel"]]:
-) -> Tuple[bool, List[Tuple[str, Any]], Optional[str], Optional[List[int]]]:
     """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
+            return False, 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.content:
-        if content := llm_response_dict.get("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:
+        return success, llm_response
-        #     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,
-        )
 
     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]]:

src/plugin_system/core/events_manager.py

@@ -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_content=llm_response.content if llm_response else None,
-            llm_response_reasoning=llm_response.get("reasoning") if llm_response else None,
+            llm_response_reasoning=llm_response.reasoning if llm_response else None,
-            llm_response_model=llm_response.get("model") if llm_response else None,
+            llm_response_model=llm_response.model if llm_response else None,
-            llm_response_tool_call=llm_response.get("tool_calls") 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_content=(llm_response.content if llm_response else None),
-            llm_response_reasoning=(llm_response.get("reasoning") if llm_response else None),
+            llm_response_reasoning=(llm_response.reasoning if llm_response else None),
-            llm_response_model=llm_response.get("model") if llm_response else None,
+            llm_response_model=(llm_response.model if llm_response else None),
-            llm_response_tool_call=(llm_response.get("tool_calls") 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,