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better：重构personinfo，使用Person类和类属性

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This commit is contained in:
SengokuCola
2025-08-12 14:33:13 +08:00
parent 1e7f3a92a6
commit ae254de494
21 changed files with 468 additions and 1202 deletions
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12
src/chat/chat_loop/heartFC_chat.py
View File

@@ -17,7 +17,7 @@ from src.chat.planner_actions.action_manager import ActionManager
from src.chat.chat_loop.hfc_utils import CycleDetail
from src.person_info.relationship_builder_manager import relationship_builder_manager
from src.chat.express.expression_learner import expression_learner_manager
from src.person_info.person_info import get_person_info_manager
from src.person_info.person_info import Person
from src.person_info.group_relationship_manager import get_group_relationship_manager
from src.plugin_system.base.component_types import ChatMode, EventType
from src.plugin_system.core import events_manager
@@ -306,20 +306,14 @@ class HeartFChatting:
with Timer("回复发送", cycle_timers):
reply_text = await self._send_response(response_set, action_message)
# 存储reply action信息
person_info_manager = get_person_info_manager()
# 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值
platform = action_message.get("chat_info_platform")
if platform is None:
platform = getattr(self.chat_stream, "platform", "unknown")
person_id = person_info_manager.get_person_id(
platform,
action_message.get("user_id", ""),
)
person_name = await person_info_manager.get_value(person_id, "person_name")
person = Person(platform = platform ,user_id = action_message.get("user_id", ""))
person_name = person.person_name
action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
await database_api.store_action_info(

2
src/chat/express/expression_learner.py
View File

@@ -281,7 +281,7 @@ class ExpressionLearner:
logger.info(f"在 {group_name} 学习到表达风格:\n{learnt_expressions_str}")
if not learnt_expressions:
logger.info(f"没有学习到表达风格")
logger.info("没有学习到表达风格")
return []
# 按chat_id分组

43
src/chat/express/expression_selector.py
View File

@@ -3,7 +3,7 @@ import time
import random
import hashlib
from typing import List, Dict, Tuple, Optional, Any
from typing import List, Dict, Optional, Any
from json_repair import repair_json
from src.llm_models.utils_model import LLMRequest
@@ -22,16 +22,22 @@ def init_prompt():
你的名字是{bot_name}{target_message}
你知道以下这些表达方式，梗和说话方式：
以下是可选的表达情境：
{all_situations}
现在，请你根据聊天记录从中挑选合适的表达方式，梗和说话方式，组织一条回复风格指导，指导的目的是在组织回复的时候提供一些语言风格和梗上的参考。
请在reply_style_guide中以平文本输出指导，不要浮夸，并在selected_expressions中说明在指导中你挑选了哪些表达方式，梗和说话方式，以json格式输出：
例子：
请你分析聊天内容的语境、情绪、话题类型，从上述情境中选择最适合当前聊天情境的，最多{max_num}个情境。
考虑因素包括：
1. 聊天的情绪氛围（轻松、严肃、幽默等）
2. 话题类型（日常、技术、游戏、情感等）
3. 情境与当前语境的匹配度
{target_message_extra_block}
请以JSON格式输出，只需要输出选中的情境编号：
例如：
{{
"reply_style_guide": "...",
"selected_expressions": [2, 3, 4, 7]
"selected_situations": [2, 3, 5, 7, 19]
}}
请严格按照JSON格式输出，不要包含其他内容：
"""
Prompt(expression_evaluation_prompt, "expression_evaluation_prompt")
@@ -190,14 +196,14 @@ class ExpressionSelector:
chat_info: str,
max_num: int = 10,
target_message: Optional[str] = None,
) -> Tuple[str, List[Dict[str, Any]]]:
) -> List[Dict[str, Any]]:
# sourcery skip: inline-variable, list-comprehension
"""使用LLM选择适合的表达方式"""
# 检查是否允许在此聊天流中使用表达
if not self.can_use_expression_for_chat(chat_id):
logger.debug(f"聊天流 {chat_id} 不允许使用表达，返回空列表")
return "", []
return []
# 1. 获取20个随机表达方式（现在按权重抽取）
style_exprs = self.get_random_expressions(chat_id, 10)
@@ -216,7 +222,7 @@ class ExpressionSelector:
if not all_expressions:
logger.warning("没有找到可用的表达方式")
return "", []
return []
all_situations_str = "\n".join(all_situations)
@@ -255,24 +261,23 @@ class ExpressionSelector:
if not content:
logger.warning("LLM返回空结果")
return "", []
return []
# 5. 解析结果
result = repair_json(content)
if isinstance(result, str):
result = json.loads(result)
if not isinstance(result, dict) or "reply_style_guide" not in result or "selected_expressions" not in result:
if not isinstance(result, dict) or "selected_situations" not in result:
logger.error("LLM返回格式错误")
logger.info(f"LLM返回结果: \n{content}")
return "", []
reply_style_guide = result["reply_style_guide"]
selected_expressions = result["selected_expressions"]
return []
selected_indices = result["selected_situations"]
# 根据索引获取完整的表达方式
valid_expressions = []
for idx in selected_expressions:
for idx in selected_indices:
if isinstance(idx, int) and 1 <= idx <= len(all_expressions):
expression = all_expressions[idx - 1] # 索引从1开始
valid_expressions.append(expression)
@@ -282,11 +287,11 @@ class ExpressionSelector:
self.update_expressions_count_batch(valid_expressions, 0.006)
# logger.info(f"LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个")
return reply_style_guide, valid_expressions
return valid_expressions
except Exception as e:
logger.error(f"LLM处理表达方式选择时出错: {e}")
return "", []
return []

41
src/chat/express/expression_selector_old.py → src/chat/express/expression_selector_new.py
View File

@@ -22,22 +22,16 @@ def init_prompt():
你的名字是{bot_name}{target_message}
以下是可选的表达情境：
你知道以下这些表达方式，梗和说话方式：
{all_situations}
请你分析聊天内容的语境、情绪、话题类型，从上述情境中选择最适合当前聊天情境的，最多{max_num}个情境。
考虑因素包括：
1. 聊天的情绪氛围（轻松、严肃、幽默等）
2. 话题类型（日常、技术、游戏、情感等）
3. 情境与当前语境的匹配度
{target_message_extra_block}
请以JSON格式输出，只需要输出选中的情境编号：
例如：
现在，请你根据聊天记录从中挑选合适的表达方式，梗和说话方式，组织一条回复风格指导，指导的目的是在组织回复的时候提供一些语言风格和梗上的参考。
请在reply_style_guide中以平文本输出指导，不要浮夸，并在selected_expressions中说明在指导中你挑选了哪些表达方式，梗和说话方式，以json格式输出：
例子：
{{
"selected_situations": [2, 3, 5, 7, 19]
"reply_style_guide": "...",
"selected_expressions": [2, 3, 4, 7]
}}
请严格按照JSON格式输出，不要包含其他内容：
"""
Prompt(expression_evaluation_prompt, "expression_evaluation_prompt")
@@ -196,14 +190,14 @@ class ExpressionSelector:
chat_info: str,
max_num: int = 10,
target_message: Optional[str] = None,
) -> List[Dict[str, Any]]:
) -> Tuple[str, List[Dict[str, Any]]]:
# sourcery skip: inline-variable, list-comprehension
"""使用LLM选择适合的表达方式"""
# 检查是否允许在此聊天流中使用表达
if not self.can_use_expression_for_chat(chat_id):
logger.debug(f"聊天流 {chat_id} 不允许使用表达，返回空列表")
return []
return "", []
# 1. 获取20个随机表达方式（现在按权重抽取）
style_exprs = self.get_random_expressions(chat_id, 10)
@@ -222,7 +216,7 @@ class ExpressionSelector:
if not all_expressions:
logger.warning("没有找到可用的表达方式")
return []
return "", []
all_situations_str = "\n".join(all_situations)
@@ -261,23 +255,24 @@ class ExpressionSelector:
if not content:
logger.warning("LLM返回空结果")
return []
return "", []
# 5. 解析结果
result = repair_json(content)
if isinstance(result, str):
result = json.loads(result)
if not isinstance(result, dict) or "selected_situations" not in result:
if not isinstance(result, dict) or "reply_style_guide" not in result or "selected_expressions" not in result:
logger.error("LLM返回格式错误")
logger.info(f"LLM返回结果: \n{content}")
return []
selected_indices = result["selected_situations"]
return "", []
reply_style_guide = result["reply_style_guide"]
selected_expressions = result["selected_expressions"]
# 根据索引获取完整的表达方式
valid_expressions = []
for idx in selected_indices:
for idx in selected_expressions:
if isinstance(idx, int) and 1 <= idx <= len(all_expressions):
expression = all_expressions[idx - 1] # 索引从1开始
valid_expressions.append(expression)
@@ -287,11 +282,11 @@ class ExpressionSelector:
self.update_expressions_count_batch(valid_expressions, 0.006)
# logger.info(f"LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个")
return valid_expressions
return reply_style_guide, valid_expressions
except Exception as e:
logger.error(f"LLM处理表达方式选择时出错: {e}")
return []
return "", []

24
src/chat/heart_flow/heartflow_message_processor.py
View File

@@ -14,34 +14,14 @@ from src.chat.utils.utils import is_mentioned_bot_in_message
from src.chat.utils.timer_calculator import Timer
from src.chat.utils.chat_message_builder import replace_user_references_sync
from src.common.logger import get_logger
from src.person_info.relationship_manager import get_relationship_manager
from src.mood.mood_manager import mood_manager
from src.person_info.person_info import Person
if TYPE_CHECKING:
from src.chat.heart_flow.sub_heartflow import SubHeartflow
logger = get_logger("chat")
async def _process_relationship(message: MessageRecv) -> None:
"""处理用户关系逻辑
Args:
message: 消息对象，包含用户信息
"""
platform = message.message_info.platform
user_id = message.message_info.user_info.user_id # type: ignore
nickname = message.message_info.user_info.user_nickname # type: ignore
cardname = message.message_info.user_info.user_cardname or nickname # type: ignore
relationship_manager = get_relationship_manager()
is_known = await relationship_manager.is_known_some_one(platform, user_id)
if not is_known:
logger.info(f"首次认识用户: {nickname}")
await relationship_manager.first_knowing_some_one(platform, user_id, nickname, cardname) # type: ignore
async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool, list[str]]:
"""计算消息的兴趣度
@@ -165,7 +145,7 @@ class HeartFCMessageReceiver:
# 4. 关系处理
if global_config.relationship.enable_relationship:
await _process_relationship(message)
person = Person(platform=message.message_info.platform, user_id=message.message_info.user_info.user_id,nickname=userinfo.user_nickname)
except Exception as e:
logger.error(f"消息处理失败: {e}")

29
src/chat/replyer/default_generator.py
View File

@@ -9,7 +9,6 @@ from datetime import datetime
from src.mais4u.mai_think import mai_thinking_manager
from src.common.logger import get_logger
from src.config.config import global_config, model_config
from src.config.api_ada_configs import TaskConfig
from src.individuality.individuality import get_individuality
from src.llm_models.utils_model import LLMRequest
from src.chat.message_receive.message import UserInfo, Seg, MessageRecv, MessageSending
@@ -27,8 +26,7 @@ from src.chat.express.expression_selector import expression_selector
from src.chat.memory_system.memory_activator import MemoryActivator
from src.chat.memory_system.instant_memory import InstantMemory
from src.mood.mood_manager import mood_manager
from src.person_info.relationship_fetcher import relationship_fetcher_manager
from src.person_info.person_info import get_person_info_manager
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
@@ -302,16 +300,14 @@ class DefaultReplyer:
if not global_config.relationship.enable_relationship:
return ""
relationship_fetcher = relationship_fetcher_manager.get_fetcher(self.chat_stream.stream_id)
# 获取用户ID
person_info_manager = get_person_info_manager()
person_id = person_info_manager.get_person_id_by_person_name(sender)
person = Person(platform=self.chat_stream.platform, user_id=sender)
person_id = person.person_id
if not person_id:
logger.warning(f"未找到用户 {sender} 的ID，跳过信息提取")
return f"你完全不认识{sender}，不理解ta的相关信息。"
return await relationship_fetcher.build_relation_info(person_id, points_num=5)
return person.build_relationship(points_num=5)
async def build_expression_habits(self, chat_history: str, target: str) -> Tuple[str, str]:
"""构建表达习惯块
@@ -330,7 +326,7 @@ class DefaultReplyer:
style_habits = []
# 使用从处理器传来的选中表达方式
# LLM模式：调用LLM选择5-10个，然后随机选5个
reply_style_guide, selected_expressions = await expression_selector.select_suitable_expressions_llm(
selected_expressions = await expression_selector.select_suitable_expressions_llm(
self.chat_stream.stream_id, chat_history, max_num=8, target_message=target
)
@@ -354,7 +350,7 @@ class DefaultReplyer:
)
expression_habits_block += f"{style_habits_str}\n"
return (f"{expression_habits_title}\n{expression_habits_block}", reply_style_guide)
return f"{expression_habits_title}\n{expression_habits_block}"
async def build_memory_block(self, chat_history: str, target: str) -> str:
"""构建记忆块
@@ -659,18 +655,16 @@ class DefaultReplyer:
available_actions = {}
chat_stream = self.chat_stream
chat_id = chat_stream.stream_id
person_info_manager = get_person_info_manager()
is_group_chat = bool(chat_stream.group_info)
platform = chat_stream.platform
user_id = reply_message.get("user_id","")
if user_id:
person_id = person_info_manager.get_person_id(platform,user_id)
person_name = await person_info_manager.get_value(person_id, "person_name")
person = Person(platform=platform, user_id=user_id)
person_name = person.person_name or user_id
sender = person_name
target = reply_message.get('processed_plain_text')
else:
person_id = ""
person_name = "用户"
sender = "用户"
target = "消息"
@@ -746,7 +740,7 @@ class DefaultReplyer:
logger.warning(f"回复生成前信息获取耗时过长: {chinese_name} 耗时: {duration:.1f}s，请使用更快的模型")
logger.info(f"在回复前的步骤耗时: {'; '.join(timing_logs)}")
(expression_habits_block, reply_style_guide) = results_dict["expression_habits"]
expression_habits_block = results_dict["expression_habits"]
relation_info = results_dict["relation_info"]
memory_block = results_dict["memory_block"]
tool_info = results_dict["tool_info"]
@@ -802,7 +796,7 @@ class DefaultReplyer:
if global_config.bot.qq_account == user_id and platform == global_config.bot.platform:
return await global_prompt_manager.format_prompt(
"replyer_self_prompt",
expression_habits_block=reply_style_guide,
expression_habits_block=expression_habits_block,
tool_info_block=tool_info,
knowledge_prompt=prompt_info,
memory_block=memory_block,
@@ -822,7 +816,7 @@ class DefaultReplyer:
else:
return await global_prompt_manager.format_prompt(
"replyer_prompt",
expression_habits_block=reply_style_guide,
expression_habits_block=expression_habits_block,
tool_info_block=tool_info,
knowledge_prompt=prompt_info,
memory_block=memory_block,
@@ -885,7 +879,6 @@ class DefaultReplyer:
self.build_relation_info(sender, target),
)
expression_habits_block, reply_style_guide = expression_habits_block
keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)

3
src/chat/replyer/replyer_manager.py
View File

@@ -1,7 +1,6 @@
from typing import Dict, Optional, List, Tuple
from typing import Dict, Optional
from src.common.logger import get_logger
from src.config.api_ada_configs import TaskConfig
from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
from src.chat.replyer.default_generator import DefaultReplyer

23
src/chat/utils/chat_message_builder.py
View File

@@ -9,7 +9,7 @@ from src.config.config import global_config
from src.common.message_repository import find_messages, count_messages
from src.common.database.database_model import ActionRecords
from src.common.database.database_model import Images
from src.person_info.person_info import PersonInfoManager, get_person_info_manager
from src.person_info.person_info import Person,get_person_id
from src.chat.utils.utils import translate_timestamp_to_human_readable, assign_message_ids
install(extra_lines=3)
@@ -35,14 +35,12 @@ def replace_user_references_sync(
str: 处理后的内容字符串
"""
if name_resolver is None:
person_info_manager = get_person_info_manager()
def default_resolver(platform: str, user_id: str) -> str:
# 检查是否是机器人自己
if replace_bot_name and user_id == global_config.bot.qq_account:
return f"{global_config.bot.nickname}(你)"
person_id = PersonInfoManager.get_person_id(platform, user_id)
return person_info_manager.get_value_sync(person_id, "person_name") or user_id # type: ignore
person = Person(platform=platform, user_id=user_id)
return person.person_name or user_id # type: ignore
name_resolver = default_resolver
@@ -110,14 +108,12 @@ async def replace_user_references_async(
str: 处理后的内容字符串
"""
if name_resolver is None:
person_info_manager = get_person_info_manager()
async def default_resolver(platform: str, user_id: str) -> str:
# 检查是否是机器人自己
if replace_bot_name and user_id == global_config.bot.qq_account:
return f"{global_config.bot.nickname}(你)"
person_id = PersonInfoManager.get_person_id(platform, user_id)
return await person_info_manager.get_value(person_id, "person_name") or user_id # type: ignore
person = Person(platform=platform, user_id=user_id)
return person.person_name or user_id # type: ignore
name_resolver = default_resolver
@@ -506,14 +502,13 @@ def _build_readable_messages_internal(
if not all([platform, user_id, timestamp is not None]):
continue
person_id = PersonInfoManager.get_person_id(platform, user_id)
person_info_manager = get_person_info_manager()
person = Person(platform=platform, user_id=user_id)
# 根据 replace_bot_name 参数决定是否替换机器人名称
person_name: str
if replace_bot_name and user_id == global_config.bot.qq_account:
person_name = f"{global_config.bot.nickname}(你)"
else:
person_name = person_info_manager.get_value_sync(person_id, "person_name") # type: ignore
person_name = person.person_name or user_id # type: ignore
# 如果 person_name 未设置，则使用消息中的 nickname 或默认名称
if not person_name:
@@ -1009,7 +1004,7 @@ async def build_anonymous_messages(messages: List[Dict[str, Any]]) -> str:
# print("SELF11111111111111")
return "SELF"
try:
person_id = PersonInfoManager.get_person_id(platform, user_id)
person_id = get_person_id(platform, user_id)
except Exception as _e:
person_id = None
if not person_id:
@@ -1102,7 +1097,7 @@ async def get_person_id_list(messages: List[Dict[str, Any]]) -> List[str]:
if platform is None:
platform = "unknown"
if person_id := PersonInfoManager.get_person_id(platform, user_id):
if person_id := get_person_id(platform, user_id):
person_ids_set.add(person_id)
return list(person_ids_set) # 将集合转换为列表返回

8
src/chat/utils/utils.py
View File

@@ -15,7 +15,7 @@ from src.config.config import global_config, model_config
from src.chat.message_receive.message import MessageRecv
from src.chat.message_receive.chat_stream import get_chat_manager
from src.llm_models.utils_model import LLMRequest
from src.person_info.person_info import PersonInfoManager, get_person_info_manager
from src.person_info.person_info import Person
from .typo_generator import ChineseTypoGenerator
logger = get_logger("chat_utils")
@@ -639,12 +639,12 @@ def get_chat_type_and_target_info(chat_id: str) -> Tuple[bool, Optional[Dict]]:
# Try to fetch person info
try:
# Assume get_person_id is sync (as per original code), keep using to_thread
person_id = PersonInfoManager.get_person_id(platform, user_id)
person = Person(platform=platform, user_id=user_id)
person_id = person.person_id
person_name = None
if person_id:
# get_value is async, so await it directly
person_info_manager = get_person_info_manager()
person_name = person_info_manager.get_value_sync(person_id, "person_name")
person_name = person.person_name
target_info["person_id"] = person_id
target_info["person_name"] = person_name