320 lines
10 KiB
Python
320 lines
10 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, Optional, 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'), 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()
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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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async def find_by_group_id(self, group_id: str, limit: int = 100) -> List[StatementNode]:
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"""根据group_id查询陈述句
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Args:
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group_id: 组ID
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limit: 返回结果的最大数量
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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({"group_id": group_id}, limit=limit)
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async def search_by_embedding(
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self,
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embedding: List[float],
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group_id: Optional[str] = None,
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limit: int = 10,
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min_score: float = 0.7
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) -> List[Dict]:
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"""基于向量相似度搜索陈述句
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使用余弦相似度计算查询向量与陈述句向量的相似度。
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Args:
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embedding: 查询向量
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group_id: 可选的组ID过滤
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limit: 返回结果的最大数量
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min_score: 最小相似度分数阈值
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Returns:
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List[Dict]: 包含陈述句和相似度分数的字典列表
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每个字典包含: statement (StatementNode), score (float)
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"""
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# 构建查询条件
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where_clause = "n.statement_embedding IS NOT NULL"
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if group_id:
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where_clause += " AND n.group_id = $group_id"
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query = f"""
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MATCH (n:{self.node_label})
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WHERE {where_clause}
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WITH n, gds.similarity.cosine(n.statement_embedding, $embedding) AS score
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WHERE score > $min_score
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RETURN n, score
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ORDER BY score DESC
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LIMIT $limit
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"""
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params = {
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"embedding": embedding,
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"min_score": min_score,
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"limit": limit
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}
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if group_id:
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params["group_id"] = group_id
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results = await self.connector.execute_query(query, **params)
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return [
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{
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"statement": self._map_to_entity(r),
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"score": r.get("score", 0.0)
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}
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for r in results
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]
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async def search_by_keyword(
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self,
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keyword: str,
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group_id: Optional[str] = None,
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limit: int = 50
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) -> List[StatementNode]:
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"""基于关键词搜索陈述句
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Args:
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keyword: 搜索关键词
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group_id: 可选的组ID过滤
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limit: 返回结果的最大数量
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Returns:
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List[StatementNode]: 陈述句列表
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"""
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where_clause = "n.statement CONTAINS $keyword"
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if group_id:
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where_clause += " AND n.group_id = $group_id"
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query = f"""
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MATCH (n:{self.node_label})
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WHERE {where_clause}
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RETURN n
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LIMIT $limit
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"""
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params = {"keyword": keyword, "limit": limit}
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if group_id:
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params["group_id"] = group_id
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results = await self.connector.execute_query(query, **params)
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return [self._map_to_entity(r) for r in results]
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async def find_by_temporal_range(
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self,
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group_id: str,
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start_date: Optional[datetime] = None,
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end_date: Optional[datetime] = None,
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limit: int = 100
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) -> List[StatementNode]:
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"""根据时间范围查询陈述句
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查询在指定时间范围内有效的陈述句。
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Args:
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group_id: 组ID
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start_date: 开始日期（可选）
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end_date: 结束日期（可选）
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limit: 返回结果的最大数量
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Returns:
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List[StatementNode]: 陈述句列表
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"""
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where_clauses = ["n.group_id = $group_id"]
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params = {"group_id": group_id, "limit": limit}
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if start_date:
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where_clauses.append("n.valid_at >= $start_date")
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params["start_date"] = start_date.isoformat()
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if end_date:
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where_clauses.append("(n.invalid_at IS NULL OR n.invalid_at <= $end_date)")
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params["end_date"] = end_date.isoformat()
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where_str = " AND ".join(where_clauses)
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query = f"""
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MATCH (n:{self.node_label})
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WHERE {where_str}
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RETURN n
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ORDER BY n.created_at DESC
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LIMIT $limit
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"""
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results = await self.connector.execute_query(query, **params)
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return [self._map_to_entity(r) for r in results]
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async def find_strong_statements(
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self,
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group_id: str,
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limit: int = 100
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) -> List[StatementNode]:
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"""查询强连接的陈述句
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Args:
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group_id: 组ID
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limit: 返回结果的最大数量
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Returns:
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List[StatementNode]: 强连接的陈述句列表
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"""
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return await self.find(
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{"group_id": group_id, "connect_strength": "Strong"},
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limit=limit
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)
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async def find_by_config_id(
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self,
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config_id: str,
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limit: int = 100
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) -> List[StatementNode]:
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"""根据config_id查询陈述句
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Args:
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config_id: 配置ID
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limit: 返回结果的最大数量
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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({"config_id": config_id}, limit=limit)
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async def search_by_embedding_with_config(
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self,
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embedding: List[float],
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config_id: Optional[str] = None,
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group_id: Optional[str] = None,
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limit: int = 10,
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min_score: float = 0.7
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) -> List[Dict]:
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"""基于向量相似度搜索陈述句,可选择按config_id过滤
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使用余弦相似度计算查询向量与陈述句向量的相似度。
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支持按config_id过滤结果,确保只返回使用特定配置处理的陈述句。
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Args:
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embedding: 查询向量
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config_id: 可选的配置ID过滤
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group_id: 可选的组ID过滤
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limit: 返回结果的最大数量
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min_score: 最小相似度分数阈值
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Returns:
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List[Dict]: 包含陈述句和相似度分数的字典列表
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每个字典包含: statement (StatementNode), score (float)
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"""
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# 构建查询条件
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where_clauses = ["n.statement_embedding IS NOT NULL"]
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params = {
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"embedding": embedding,
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"min_score": min_score,
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"limit": limit
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}
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if config_id:
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where_clauses.append("n.config_id = $config_id")
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params["config_id"] = config_id
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if group_id:
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where_clauses.append("n.group_id = $group_id")
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params["group_id"] = group_id
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where_str = " AND ".join(where_clauses)
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query = f"""
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MATCH (n:{self.node_label})
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WHERE {where_str}
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WITH n, gds.similarity.cosine(n.statement_embedding, $embedding) AS score
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WHERE score > $min_score
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RETURN n, score
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ORDER BY score DESC
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LIMIT $limit
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"""
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results = await self.connector.execute_query(query, **params)
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return [
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{
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"statement": self._map_to_entity(r),
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"score": r.get("score", 0.0)
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}
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for r in results
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]
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