{{ t( "Define business entities, declare relationships, let the framework assemble your data.", "定义业务实体，声明关系，让框架组装数据。" )}}
pip install pydantic-resolve
{{ t("Components map 1:1 to Clean Architecture — dependency direction always points inward.", "组件与整洁架构一一对应，依赖方向始终指向内层。") }}
{{ t("Define business entities and relationships independent of any framework or database — the stable core everything else derives from.", "定义独立于框架和数据库的业务实体与关系——所有其他层都从中派生的稳定内核。") }}
{{ t("Automate data assembly orchestration — resolve loads missing data with batching, post computes derived fields after the tree is ready.", "自动化数据组装编排——resolve 批量加载缺失数据，post 在树就绪后计算派生字段。") }}
{{ t("Abstract data sources behind a unified interface. Swap SQLAlchemy for Django or Tortoise ORM — Entity and Response stay untouched.", "用统一接口抽象数据源。从 SQLAlchemy 切换到 Django 或 Tortoise ORM，实体和响应层无需改动。") }}
{{ t("Replace manual loops with declarative data loading.", "用声明式数据加载替代手动循环查询。") }}
# Sprint has 10 tasks, each needs owner
for task in sprint.tasks:
task.owner = await get_user(task.owner_id)
# 10 queries!class TaskView(BaseModel):
owner_id: int
owner: Optional[UserView] = None
def resolve_owner(self, loader=Loader(user_loader)):
return loader.load(self.owner_id)
# 1 query!class UserEntity(BaseModel, BaseEntity):
id: int
name: str
class TaskEntity(BaseModel, BaseEntity):
__relationships__ = [
Relationship(fk='owner_id', target=UserEntity, loader=user_loader),
]
id: int
name: str
owner_id: int
class StoryEntity(BaseModel, BaseEntity):
__relationships__ = [
Relationship(fk='id', target=list[TaskEntity], loader=task_loader),
]
id: int
name: strclass TaskSummary(DefineSubset):
__subset__ = (TaskEntity, ('id', 'name'))
owner: Optional[UserEntity] = None # implicit match, no AutoLoad
class StoryView(DefineSubset):
__subset__ = (StoryEntity, ('id', 'name'))
tasks: list[TaskSummary] = [] # implicit match@router.get("/stories")
async def get_stories():
stories = [StoryView.model_validate(s)
for s in await get_stories_from_db()]
return await Resolver().resolve(stories)[
{
"id": 1,
"name": "Sprint 1",
"tasks": [
{
"id": 101,
"name": "Implement login",
"owner": { "id": 1, "name": "Alice" }
}
]
}
]{{ t("Start simple, scale as needed. Each concept builds on the last.", "从简单开始，按需扩展。每个概念都建立在前一个之上。") }}
{{ t("From loading related data to generating GraphQL APIs — all in one library.", "从加载关联数据到生成 GraphQL API — 一个库全覆盖。") }}
{{ t("Auto-batch queries via DataLoader pattern. 100 lookups become 1 query.", "通过 DataLoader 模式自动批量查询，100 次查询变 1 次。") }}
{{ t("Compute derived fields after all nested data is resolved. Counts, aggregates, formatting.", "在嵌套数据就绪后计算派生字段：计数、聚合、格式化。") }}
{{ t("ExposeAs, SendTo, Collector — pass context down or aggregate data up without traversal code.", "ExposeAs / SendTo / Collector，无需手写遍历逻辑即可跨层传递数据。") }}
{{ t("Centralize relationship declarations. Reuse across models, GraphQL, and MCP services.", "集中管理关系声明，跨模型复用，同时驱动 GraphQL 和 MCP。") }}
{{ t("Auto-generate GraphQL schema and resolvers from your ER Diagram.", "从 ER 图自动生成 GraphQL schema 和 resolver。") }}
{{ t("Expose GraphQL APIs to AI agents via Model Context Protocol.", "通过 MCP 协议将 GraphQL API 暴露给 AI 代理。") }}
{{ t("Works with your existing frameworks and ORMs.", "与你现有的框架和 ORM 无缝集成。") }}
{{ t("Every feature is covered — from core resolution to ER Diagram, cross-layer data flow, and edge cases.", "每个功能都有覆盖——从核心解析到 ER 图、跨层数据流和边界情况。") }}
{{ t("Entity-First architecture keeps your API independent of database changes. No more cascade refactoring.", "Entity-First 架构让 API 独立于数据库变更，不再级联重构。") }}
{{ t("Every commit passes automated CI. Python 3.10–3.13 compatible with continuous integration.", "每次提交通过自动化 CI，兼容 Python 3.10–3.13。") }}
{{ t("Define entities, declare relationships, let the framework assemble. Start from resolve_*, scale to ER Diagram when ready.", "定义实体，声明关系，让框架组装。从 resolve_* 开始，准备好了再扩展到 ER 图。") }}