1f4524c28c
feature/情绪引擎 * feature/emotion-engine: (7 commits squashed) - [feature]Emotion Engine Development - [feature]Emotion Engine Development - Merge branch 'feature/emotion-engine' of codeup.aliyun.com:redbearai/python/redbear-mem-open into feature/emotion-engine - [fix]1.Fix the front-end files;2.Cache Management Deletion;3.Delete "check_code.py" - [fix]1.Fix the front-end files;2.Cache Management Deletion;3.Delete "check_code.py" - Merge branch 'feature/emotion-engine' of codeup.aliyun.com:redbearai/python/redbear-mem-open into feature/emotion-engine - [fix]fix vite.config.ts Signed-off-by: 乐力齐 <accounts_690c7b0af9007d7e338af636@mail.teambition.com> Commented-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com> Commented-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/21
90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
# -*- coding: utf-8 -*-
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"""陈述句仓储模块
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本模块提供陈述句节点的数据访问功能。
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Classes:
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StatementRepository: 陈述句仓储，管理StatementNode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 StatementNode
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from app.core.memory.utils.data.ontology import TemporalInfo
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class StatementRepository(BaseNeo4jRepository[StatementNode]):
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"""陈述句仓储
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管理陈述句节点的创建、查询、更新和删除操作。
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提供按chunk_id、group_id、向量相似度等条件查询陈述句的方法。
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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"Statement"
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"""
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def __init__(self, connector: Neo4jConnector):
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"""初始化陈述句仓储
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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, "Statement")
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def _map_to_entity(self, node_data: Dict) -> StatementNode:
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"""将节点数据映射为陈述句实体
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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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StatementNode: 陈述句实体对象
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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['expired_at'], str):
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n['expired_at'] = datetime.fromisoformat(n['expired_at'])
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if n.get('valid_at') and isinstance(n['valid_at'], str):
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n['valid_at'] = datetime.fromisoformat(n['valid_at'])
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if n.get('invalid_at') and isinstance(n['invalid_at'], str):
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n['invalid_at'] = datetime.fromisoformat(n['invalid_at'])
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# 处理temporal_info字段
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if isinstance(n.get('temporal_info'), str):
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# 从字符串转换为枚举值
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n['temporal_info'] = TemporalInfo(n['temporal_info'])
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elif isinstance(n.get('temporal_info'), dict):
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n['temporal_info'] = TemporalInfo(**n['temporal_info'])
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elif not n.get('temporal_info'):
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# 如果没有temporal_info，创建一个默认的
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n['temporal_info'] = TemporalInfo.STATIC
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# 处理情绪字段 - 映射 Neo4j 节点属性到 StatementNode 模型
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# 处理空值情况，确保字段存在
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n['emotion_type'] = n.get('emotion_type')
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n['emotion_intensity'] = n.get('emotion_intensity')
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n['emotion_keywords'] = n.get('emotion_keywords', [])
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n['emotion_subject'] = n.get('emotion_subject')
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n['emotion_target'] = n.get('emotion_target')
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return StatementNode(**n)
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async def find_by_chunk_id(self, chunk_id: str) -> List[StatementNode]:
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"""根据chunk_id查询陈述句
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Args:
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chunk_id: 分块ID
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Returns:
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List[StatementNode]: 陈述句列表
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"""
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return await self.find({"chunk_id": chunk_id})
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