# -*- coding: utf-8 -*- """陈述句仓储模块 本模块提供陈述句节点的数据访问功能。 Classes: StatementRepository: 陈述句仓储，管理StatementNode的CRUD操作 """ from typing import List, Optional, Dict from datetime import datetime from app.repositories.neo4j.base_neo4j_repository import BaseNeo4jRepository from app.core.memory.models.graph_models import StatementNode from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.core.memory.utils.data.ontology import TemporalInfo class StatementRepository(BaseNeo4jRepository[StatementNode]): """陈述句仓储 管理陈述句节点的创建、查询、更新和删除操作。 提供按chunk_id、group_id、向量相似度等条件查询陈述句的方法。 Attributes: connector: Neo4j连接器实例 node_label: 节点标签，固定为"Statement" """ def __init__(self, connector: Neo4jConnector): """初始化陈述句仓储 Args: connector: Neo4j连接器实例 """ super().__init__(connector, "Statement") def _map_to_entity(self, node_data: Dict) -> StatementNode: """将节点数据映射为陈述句实体 Args: node_data: 从Neo4j查询返回的节点数据字典 Returns: StatementNode: 陈述句实体对象 """ # 从查询结果中提取节点数据 n = node_data.get('n', node_data) # 处理datetime字段 if isinstance(n.get('created_at'), str): n['created_at'] = datetime.fromisoformat(n['created_at']) if n.get('expired_at') and isinstance(n['expired_at'], str): n['expired_at'] = datetime.fromisoformat(n['expired_at']) if n.get('valid_at') and isinstance(n['valid_at'], str): n['valid_at'] = datetime.fromisoformat(n['valid_at']) if n.get('invalid_at') and isinstance(n['invalid_at'], str): n['invalid_at'] = datetime.fromisoformat(n['invalid_at']) # 处理temporal_info字段 if isinstance(n.get('temporal_info'), dict): n['temporal_info'] = TemporalInfo(**n['temporal_info']) elif not n.get('temporal_info'): # 如果没有temporal_info，创建一个默认的 n['temporal_info'] = TemporalInfo() return StatementNode(**n) async def find_by_chunk_id(self, chunk_id: str) -> List[StatementNode]: """根据chunk_id查询陈述句 Args: chunk_id: 分块ID Returns: List[StatementNode]: 陈述句列表 """ return await self.find({"chunk_id": chunk_id}) async def find_by_group_id(self, group_id: str, limit: int = 100) -> List[StatementNode]: """根据group_id查询陈述句 Args: group_id: 组ID limit: 返回结果的最大数量 Returns: List[StatementNode]: 陈述句列表 """ return await self.find({"group_id": group_id}, limit=limit) async def search_by_embedding( self, embedding: List[float], group_id: Optional[str] = None, limit: int = 10, min_score: float = 0.7 ) -> List[Dict]: """基于向量相似度搜索陈述句 使用余弦相似度计算查询向量与陈述句向量的相似度。 Args: embedding: 查询向量 group_id: 可选的组ID过滤 limit: 返回结果的最大数量 min_score: 最小相似度分数阈值 Returns: List[Dict]: 包含陈述句和相似度分数的字典列表 每个字典包含: statement (StatementNode), score (float) """ # 构建查询条件 where_clause = "n.statement_embedding IS NOT NULL" if group_id: where_clause += " AND n.group_id = $group_id" query = f""" MATCH (n:{self.node_label}) WHERE {where_clause} WITH n, gds.similarity.cosine(n.statement_embedding, $embedding) AS score WHERE score > $min_score RETURN n, score ORDER BY score DESC LIMIT $limit """ params = { "embedding": embedding, "min_score": min_score, "limit": limit } if group_id: params["group_id"] = group_id results = await self.connector.execute_query(query, **params) return [ { "statement": self._map_to_entity(r), "score": r.get("score", 0.0) } for r in results ] async def search_by_keyword( self, keyword: str, group_id: Optional[str] = None, limit: int = 50 ) -> List[StatementNode]: """基于关键词搜索陈述句 Args: keyword: 搜索关键词 group_id: 可选的组ID过滤 limit: 返回结果的最大数量 Returns: List[StatementNode]: 陈述句列表 """ where_clause = "n.statement CONTAINS $keyword" if group_id: where_clause += " AND n.group_id = $group_id" query = f""" MATCH (n:{self.node_label}) WHERE {where_clause} RETURN n LIMIT $limit """ params = {"keyword": keyword, "limit": limit} if group_id: params["group_id"] = group_id results = await self.connector.execute_query(query, **params) return [self._map_to_entity(r) for r in results] async def find_by_temporal_range( self, group_id: str, start_date: Optional[datetime] = None, end_date: Optional[datetime] = None, limit: int = 100 ) -> List[StatementNode]: """根据时间范围查询陈述句 查询在指定时间范围内有效的陈述句。 Args: group_id: 组ID start_date: 开始日期（可选） end_date: 结束日期（可选） limit: 返回结果的最大数量 Returns: List[StatementNode]: 陈述句列表 """ where_clauses = ["n.group_id = $group_id"] params = {"group_id": group_id, "limit": limit} if start_date: where_clauses.append("n.valid_at >= $start_date") params["start_date"] = start_date.isoformat() if end_date: where_clauses.append("(n.invalid_at IS NULL OR n.invalid_at <= $end_date)") params["end_date"] = end_date.isoformat() where_str = " AND ".join(where_clauses) query = f""" MATCH (n:{self.node_label}) WHERE {where_str} RETURN n ORDER BY n.created_at DESC LIMIT $limit """ results = await self.connector.execute_query(query, **params) return [self._map_to_entity(r) for r in results] async def find_strong_statements( self, group_id: str, limit: int = 100 ) -> List[StatementNode]: """查询强连接的陈述句 Args: group_id: 组ID limit: 返回结果的最大数量 Returns: List[StatementNode]: 强连接的陈述句列表 """ return await self.find( {"group_id": group_id, "connect_strength": "Strong"}, limit=limit ) async def find_by_config_id( self, config_id: str, limit: int = 100 ) -> List[StatementNode]: """根据config_id查询陈述句 Args: config_id: 配置ID limit: 返回结果的最大数量 Returns: List[StatementNode]: 陈述句列表 """ return await self.find({"config_id": config_id}, limit=limit) async def search_by_embedding_with_config( self, embedding: List[float], config_id: Optional[str] = None, group_id: Optional[str] = None, limit: int = 10, min_score: float = 0.7 ) -> List[Dict]: """基于向量相似度搜索陈述句,可选择按config_id过滤 使用余弦相似度计算查询向量与陈述句向量的相似度。 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。 Args: embedding: 查询向量 config_id: 可选的配置ID过滤 group_id: 可选的组ID过滤 limit: 返回结果的最大数量 min_score: 最小相似度分数阈值 Returns: List[Dict]: 包含陈述句和相似度分数的字典列表 每个字典包含: statement (StatementNode), score (float) """ # 构建查询条件 where_clauses = ["n.statement_embedding IS NOT NULL"] params = { "embedding": embedding, "min_score": min_score, "limit": limit } if config_id: where_clauses.append("n.config_id = $config_id") params["config_id"] = config_id if group_id: where_clauses.append("n.group_id = $group_id") params["group_id"] = group_id where_str = " AND ".join(where_clauses) query = f""" MATCH (n:{self.node_label}) WHERE {where_str} WITH n, gds.similarity.cosine(n.statement_embedding, $embedding) AS score WHERE score > $min_score RETURN n, score ORDER BY score DESC LIMIT $limit """ results = await self.connector.execute_query(query, **params) return [ { "statement": self._map_to_entity(r), "score": r.get("score", 0.0) } for r in results ]