e8a5cfe7e3
[feature]actr-记忆遗忘需求开发
* feature/actr-forget: (12 commits squashed)
- [feature]
1.Extended fields of the date_config table;
2.New activation value calculation has been added, and the ACTR parameter has been introduced in Neo4j.
- [feature]1.Create a forgetting strategy executor;2.Create the forgetting scheduler
- [feature]Introduce activation values for retrieval, and develop a two-stage retrieval reordering process
- [feature]
1.Extended fields of the date_config table;
2.New activation value calculation has been added, and the ACTR parameter has been introduced in Neo4j.
- [feature]1.Create a forgetting strategy executor;2.Create the forgetting scheduler
- [feature]Introduce activation values for retrieval, and develop a two-stage retrieval reordering process
- Merge branch 'feature/actr-forget' of codeup.aliyun.com:redbearai/python/redbear-mem-open into feature/actr-forget
- [fix]Eliminate the interference caused by redundant code
- [feature]
1.Extended fields of the date_config table;
2.New activation value calculation has been added, and the ACTR parameter has been introduced in Neo4j.
- [feature]1.Create a forgetting strategy executor;2.Create the forgetting scheduler
- [feature]Introduce activation values for retrieval, and develop a two-stage retrieval reordering process
- Merge branch 'feature/actr-forget' of codeup.aliyun.com:redbearai/python/redbear-mem-open into feature/actr-forget
Signed-off-by: 乐力齐 <accounts_690c7b0af9007d7e338af636@mail.teambition.com>
Reviewed-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com>
Merged-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com>
CR-link: https://codeup.aliyun.com/redbearai/python/redbear-mem-open/change/85
80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
"""实体仓储模块
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本模块提供实体节点的数据访问功能。
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Classes:
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EntityRepository: 实体仓储，管理ExtractedEntityNode的CRUD操作
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"""
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from typing import List, Dict
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from datetime import datetime
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from app.repositories.neo4j.base_neo4j_repository import BaseNeo4jRepository
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from app.core.memory.models.graph_models import ExtractedEntityNode
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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class EntityRepository(BaseNeo4jRepository[ExtractedEntityNode]):
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"""实体仓储
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管理实体节点的创建、查询、更新和删除操作。
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提供按类型、名称、向量相似度等条件查询实体的方法。
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Attributes:
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connector: Neo4j连接器实例
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node_label: 节点标签，固定为"ExtractedEntity"
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"""
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def __init__(self, connector: Neo4jConnector):
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"""初始化实体仓储
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Args:
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connector: Neo4j连接器实例
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"""
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super().__init__(connector, "ExtractedEntity")
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def _map_to_entity(self, node_data: Dict) -> ExtractedEntityNode:
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"""将节点数据映射为实体对象
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Args:
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node_data: 从Neo4j查询返回的节点数据字典
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Returns:
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ExtractedEntityNode: 实体对象
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"""
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# 从查询结果中提取节点数据
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n = node_data.get('n', node_data)
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# 处理datetime字段
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if isinstance(n.get('created_at'), str):
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n['created_at'] = datetime.fromisoformat(n['created_at'])
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if n.get('expired_at') and isinstance(n.get('expired_at'), str):
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n['expired_at'] = datetime.fromisoformat(n['expired_at'])
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# 确保aliases字段存在且为列表
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if 'aliases' not in n or n['aliases'] is None:
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n['aliases'] = []
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# 处理 ACT-R 属性 - 确保字段存在且有默认值
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n['importance_score'] = n.get('importance_score', 0.5)
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n['activation_value'] = n.get('activation_value')
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n['access_history'] = n.get('access_history', [])
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n['last_access_time'] = n.get('last_access_time')
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n['access_count'] = n.get('access_count', 0)
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return ExtractedEntityNode(**n)
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async def find_by_type(self, entity_type: str, limit: int = 100) -> List[ExtractedEntityNode]:
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"""根据实体类型查询
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Args:
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entity_type: 实体类型（如"Person", "Organization"等）
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limit: 返回结果的最大数量
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Returns:
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List[ExtractedEntityNode]: 实体列表
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"""
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return await self.find({"entity_type": entity_type}, limit=limit)
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