mirror of https://github.com/Mai-with-u/MaiBot.git
remove：移除relation内容
SengokuCola
parent
bf2ab6759c
commit
94405856ff
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@ -1,34 +0,0 @@
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{
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"manifest_version": 1,
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"name": "Relation插件 (Relation Actions)",
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"version": "1.0.0",
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"description": "可以构建和管理关系",
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"author": {
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"name": "SengokuCola",
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"url": "https://github.com/MaiM-with-u"
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},
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"license": "GPL-v3.0-or-later",
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"host_application": {
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"min_version": "0.10.0"
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},
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"homepage_url": "https://github.com/MaiM-with-u/maibot",
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"repository_url": "https://github.com/MaiM-with-u/maibot",
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"keywords": ["relation", "action", "built-in"],
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"categories": ["Relation"],
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"default_locale": "zh-CN",
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"locales_path": "_locales",
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"plugin_info": {
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"is_built_in": true,
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"plugin_type": "action_provider",
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"components": [
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{
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"type": "action",
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"name": "relation",
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"description": "发送关系"
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}
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]
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}
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}
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@ -1,96 +0,0 @@
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from typing import List, Tuple, Type, Any
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# 导入新插件系统
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from src.plugin_system import BasePlugin, register_plugin, ComponentInfo
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from src.plugin_system.base.config_types import ConfigField
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from src.person_info.person_info import Person
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from src.plugin_system.base.base_tool import BaseTool, ToolParamType
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# 导入依赖的系统组件
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from src.common.logger import get_logger
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from src.plugins.built_in.relation.relation import BuildRelationAction
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logger = get_logger("relation_actions")
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class GetPersonInfoTool(BaseTool):
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"""获取用户信息"""
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name = "get_person_info"
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description = "获取某个人的信息，包括印象，特征点，与用户的关系等等"
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parameters = [
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("person_name", ToolParamType.STRING, "需要获取信息的人的名称", True, None),
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("info_type", ToolParamType.STRING, "需要获取信息的类型", True, None),
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]
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available_for_llm = True
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async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
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"""执行比较两个数的大小
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Args:
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function_args: 工具参数
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Returns:
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dict: 工具执行结果
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"""
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person_name: str = function_args.get("person_name") # type: ignore
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info_type: str = function_args.get("info_type") # type: ignore
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person = Person(person_name=person_name)
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if not person:
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return {"content": f"用户 {person_name} 不存在"}
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if not person.is_known:
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return {"content": f"不认识用户 {person_name}"}
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relation_str = await person.build_relationship(info_type=info_type)
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return {"content": relation_str}
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@register_plugin
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class RelationActionsPlugin(BasePlugin):
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"""关系动作插件
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系统内置插件，提供基础的聊天交互功能：
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- Reply: 回复动作
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- NoReply: 不回复动作
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- Emoji: 表情动作
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注意：插件基本信息优先从_manifest.json文件中读取
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"""
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# 插件基本信息
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plugin_name: str = "relation_actions" # 内部标识符
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enable_plugin: bool = True
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dependencies: list[str] = [] # 插件依赖列表
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python_dependencies: list[str] = [] # Python包依赖列表
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config_file_name: str = "config.toml"
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# 配置节描述
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config_section_descriptions = {
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"plugin": "插件启用配置",
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"components": "核心组件启用配置",
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}
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# 配置Schema定义
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config_schema: dict = {
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"plugin": {
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"enabled": ConfigField(type=bool, default=False, description="是否启用插件"),
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"config_version": ConfigField(type=str, default="1.0.2", description="配置文件版本"),
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},
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"components": {
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"relation_max_memory_num": ConfigField(type=int, default=10, description="关系记忆最大数量"),
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},
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}
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def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
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"""返回插件包含的组件列表"""
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# --- 根据配置注册组件 ---
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components = []
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# components.append((BuildRelationAction.get_action_info(), BuildRelationAction))
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# components.append((GetPersonInfoTool.get_tool_info(), GetPersonInfoTool))
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return components
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@ -1,230 +0,0 @@
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import json
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from json_repair import repair_json
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from typing import Tuple
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import time
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from src.common.logger import get_logger
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from src.config.config import global_config
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from src.person_info.person_info import Person, get_memory_content_from_memory, get_weight_from_memory
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.plugin_system import BaseAction, ActionActivationType
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from src.plugin_system.apis import llm_api
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logger = get_logger("relation")
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def init_prompt():
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Prompt(
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"""
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以下是一些记忆条目的分类：
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----------------------
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{category_list}
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----------------------
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每一个分类条目类型代表了你对用户："{person_name}"的印象的一个类别
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现在，你有一条对 {person_name} 的新记忆内容：
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{memory_point}
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请判断该记忆内容是否属于上述分类，请给出分类的名称。
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如果不属于上述分类，请输出一个合适的分类名称，对新记忆内容进行概括。要求分类名具有概括性。
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注意分类数一般不超过5个
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请严格用json格式输出，不要输出任何其他内容：
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{{
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"category": "分类名称"
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}} """,
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"relation_category",
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)
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Prompt(
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"""
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以下是有关{category}的现有记忆：
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----------------------
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{memory_list}
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----------------------
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现在，你有一条对 {person_name} 的新记忆内容：
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{memory_point}
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请判断该新记忆内容是否已经存在于现有记忆中，你可以对现有进行进行以下修改：
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注意，一般来说记忆内容不超过5个，且记忆文本不应太长
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1.新增：当记忆内容不存在于现有记忆，且不存在矛盾，请用json格式输出：
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{{
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"new_memory": "需要新增的记忆内容"
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}}
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2.加深印象：如果这个新记忆已经存在于现有记忆中，在内容上与现有记忆类似，请用json格式输出：
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{{
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"memory_id": 1, #请输出你认为需要加深印象的，与新记忆内容类似的，已经存在的记忆的序号
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"integrate_memory": "加深后的记忆内容，合并内容类似的新记忆和旧记忆"
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}}
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3.整合：如果这个新记忆与现有记忆产生矛盾，请你结合其他记忆进行整合，用json格式输出：
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{{
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"memory_id": 1, #请输出你认为需要整合的，与新记忆存在矛盾的，已经存在的记忆的序号
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"integrate_memory": "整合后的记忆内容，合并内容矛盾的新记忆和旧记忆"
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}}
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现在，请你根据情况选出合适的修改方式，并输出json，不要输出其他内容：
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""",
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"relation_category_update",
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)
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class BuildRelationAction(BaseAction):
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"""关系动作 - 构建关系"""
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activation_type = ActionActivationType.LLM_JUDGE
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parallel_action = True
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# 动作基本信息
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action_name = "build_relation"
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action_description = "了解对于某人的记忆，并添加到你对对方的印象中"
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# 动作参数定义
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action_parameters = {"person_name": "需要了解或记忆的人的名称", "impression": "需要了解的对某人的记忆或印象"}
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# 动作使用场景
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action_require = [
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"了解对于某人的记忆，并添加到你对对方的印象中",
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"对方与有明确提到有关其自身的事件",
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"对方有提到其个人信息，包括喜好，身份，等等",
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"对方希望你记住对方的信息",
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]
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# 关联类型
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associated_types = ["text"]
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async def execute(self) -> Tuple[bool, str]:
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"""执行关系动作"""
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try:
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# 1. 获取构建关系的原因
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impression = self.action_data.get("impression", "")
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logger.info(f"{self.log_prefix} 添加关系印象原因: {self.reasoning}")
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person_name = self.action_data.get("person_name", "")
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# 2. 获取目标用户信息
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person = Person(person_name=person_name)
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if not person.is_known:
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logger.warning(f"{self.log_prefix} 用户 {person_name} 不存在，跳过添加记忆")
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return False, f"用户 {person_name} 不存在，跳过添加记忆"
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person.last_know = time.time()
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person.know_times += 1
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person.sync_to_database()
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category_list = person.get_all_category()
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if not category_list:
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category_list_str = "无分类"
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else:
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category_list_str = "\n".join(category_list)
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prompt = await global_prompt_manager.format_prompt(
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"relation_category",
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category_list=category_list_str,
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memory_point=impression,
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person_name=person.person_name,
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)
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if global_config.debug.show_prompt:
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logger.info(f"{self.log_prefix} 生成的LLM Prompt: {prompt}")
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else:
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logger.debug(f"{self.log_prefix} 生成的LLM Prompt: {prompt}")
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# 5. 调用LLM
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models = llm_api.get_available_models()
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chat_model_config = models.get("utils_small") # 使用字典访问方式
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if not chat_model_config:
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logger.error(f"{self.log_prefix} 未找到'utils_small'模型配置，无法调用LLM")
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return False, "未找到'utils_small'模型配置"
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success, category, _, _ = await llm_api.generate_with_model(
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prompt, model_config=chat_model_config, request_type="relation.category"
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)
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category_data = json.loads(repair_json(category))
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category = category_data.get("category", "")
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if not category:
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logger.warning(f"{self.log_prefix} LLM未给出分类，跳过添加记忆")
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return False, "LLM未给出分类，跳过添加记忆"
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# 第二部分：更新记忆
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memory_list = person.get_memory_list_by_category(category)
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if not memory_list:
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logger.info(f"{self.log_prefix} {person.person_name} 的 {category} 的记忆为空，进行创建")
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person.memory_points.append(f"{category}:{impression}:1.0")
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person.sync_to_database()
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return True, f"未找到分类为{category}的记忆点，进行添加"
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memory_list_str = ""
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memory_list_id = {}
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for id, memory in enumerate(memory_list, start=1):
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memory_content = get_memory_content_from_memory(memory)
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memory_list_str += f"{id}. {memory_content}\n"
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memory_list_id[id] = memory
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prompt = await global_prompt_manager.format_prompt(
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"relation_category_update",
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category=category,
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memory_list=memory_list_str,
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memory_point=impression,
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person_name=person.person_name,
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)
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if global_config.debug.show_prompt:
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logger.info(f"{self.log_prefix} 生成的LLM Prompt: {prompt}")
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else:
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logger.debug(f"{self.log_prefix} 生成的LLM Prompt: {prompt}")
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chat_model_config = models.get("utils")
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success, update_memory, _, _ = await llm_api.generate_with_model(
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prompt,
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model_config=chat_model_config, # type: ignore
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request_type="relation.category.update", # type: ignore
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)
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update_memory_data = json.loads(repair_json(update_memory))
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new_memory = update_memory_data.get("new_memory", "")
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memory_id = update_memory_data.get("memory_id", "")
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integrate_memory = update_memory_data.get("integrate_memory", "")
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if new_memory:
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# 新记忆
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person.memory_points.append(f"{category}:{new_memory}:1.0")
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person.sync_to_database()
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logger.info(f"{self.log_prefix} 为{person.person_name}新增记忆点: {new_memory}")
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return True, f"为{person.person_name}新增记忆点: {new_memory}"
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elif memory_id and integrate_memory:
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# 现存或冲突记忆
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memory = memory_list_id[memory_id]
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memory_content = get_memory_content_from_memory(memory)
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del_count = person.del_memory(category, memory_content)
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if del_count > 0:
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# logger.info(f"{self.log_prefix} 删除记忆点: {memory_content}")
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memory_weight = get_weight_from_memory(memory)
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person.memory_points.append(f"{category}:{integrate_memory}:{memory_weight + 1.0}")
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person.sync_to_database()
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logger.info(
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f"{self.log_prefix} 更新{person.person_name}的记忆点: {memory_content} -> {integrate_memory}"
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)
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return True, f"更新{person.person_name}的记忆点: {memory_content} -> {integrate_memory}"
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else:
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logger.warning(f"{self.log_prefix} 删除记忆点失败: {memory_content}")
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return False, f"删除{person.person_name}的记忆点失败: {memory_content}"
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return True, "关系动作执行成功"
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except Exception as e:
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logger.error(f"{self.log_prefix} 关系构建动作执行失败: {e}", exc_info=True)
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return False, f"关系动作执行失败: {str(e)}"
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# 还缺一个关系的太多遗忘和对应的提取
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init_prompt()
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