freedakgmail
119 lines
3.8 KiB
Python
119 lines
3.8 KiB
Python
"""知识图谱仓储：实体/关系写入与多跳穿透（递归 CTE）。
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对应需求 R2：支撑隐性实控人、关联方网络、"马甲"供应商等穿透分析。
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统一穿透查询服务（P1.2.5）在此之上封装对外 API，对上层屏蔽底层是关系表还是图库。
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"""
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from __future__ import annotations
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import uuid
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from sqlalchemy import text
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from sqlalchemy.orm import Session
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from app.datahub.models import Entity, EntityRelationship
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from app.datahub.ontology import EntityType, RelationshipType, is_valid_relationship
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class OntologyViolationError(ValueError):
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"""关系不符合本体约束。"""
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def upsert_entity(
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session: Session,
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entity_type: EntityType,
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business_key: str,
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display_name: str | None = None,
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attributes: dict | None = None,
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data_version_id: uuid.UUID | None = None,
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) -> Entity:
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"""按 (类型, 业务主键) 幂等写入实体（主数据对齐的归一锚点）。"""
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existing = (
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session.query(Entity)
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.filter(Entity.entity_type == entity_type.value, Entity.business_key == business_key)
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.one_or_none()
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)
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if existing is not None:
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if display_name is not None:
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existing.display_name = display_name
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if attributes:
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existing.attributes = {**(existing.attributes or {}), **attributes}
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return existing
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entity = Entity(
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entity_type=entity_type.value,
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business_key=business_key,
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display_name=display_name,
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attributes=attributes or {},
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data_version_id=data_version_id,
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)
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session.add(entity)
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session.flush()
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return entity
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def add_relationship(
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session: Session,
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rel_type: RelationshipType,
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source: Entity,
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target: Entity,
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attributes: dict | None = None,
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data_version_id: uuid.UUID | None = None,
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) -> EntityRelationship:
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"""新增一条关系边，写入前校验本体约束。"""
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src_type = EntityType(source.entity_type)
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tgt_type = EntityType(target.entity_type)
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if not is_valid_relationship(rel_type, src_type, tgt_type):
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raise OntologyViolationError(
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f"关系 {rel_type.value} 不允许从 {src_type.value} 指向 {tgt_type.value}"
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)
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rel = EntityRelationship(
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rel_type=rel_type.value,
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source_id=source.id,
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target_id=target.id,
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attributes=attributes or {},
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data_version_id=data_version_id,
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)
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session.add(rel)
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session.flush()
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return rel
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# 多跳穿透：以无向方式遍历关系边，返回与起点在 max_depth 跳内连通的实体集合。
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# 用于"疑似同一实控人/关联方网络"识别。
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_TRAVERSE_SQL = text(
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"""
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WITH RECURSIVE reachable(entity_id, depth, path) AS (
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SELECT :start_id, 0, ARRAY[:start_id]
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UNION ALL
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SELECT
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CASE WHEN r.source_id = rc.entity_id THEN r.target_id ELSE r.source_id END,
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rc.depth + 1,
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rc.path || CASE WHEN r.source_id = rc.entity_id THEN r.target_id ELSE r.source_id END
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FROM reachable rc
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JOIN entity_relationship r
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ON (r.source_id = rc.entity_id OR r.target_id = rc.entity_id)
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WHERE rc.depth < :max_depth
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AND NOT (
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CASE WHEN r.source_id = rc.entity_id THEN r.target_id ELSE r.source_id END
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= ANY(rc.path)
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)
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)
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SELECT DISTINCT entity_id, MIN(depth) AS depth
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FROM reachable
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WHERE entity_id <> :start_id
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GROUP BY entity_id
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ORDER BY depth;
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"""
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)
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def find_related_entities(
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session: Session, start_id: uuid.UUID, max_depth: int = 3
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) -> list[tuple[uuid.UUID, int]]:
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"""返回与起点实体在 max_depth 跳内连通的实体 (id, 最短跳数) 列表。"""
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rows = session.execute(
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_TRAVERSE_SQL, {"start_id": start_id, "max_depth": max_depth}
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).all()
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return [(r[0], r[1]) for r in rows]
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