Pydantic-resolve is a Pydantic based approach to construct complex data without imperative glue code.

Its best use case is building complex API data, in UI integration scenarios, it can be used as a replacement for GraphQL, reusing most of the code while offering better performance and maintainability.

It introduces resolve hooks for on-demand data fetching, and post hooks for normalization, transformation, and reorganization to meet diverse requirements.

Starting from pydantic-resolve v2, ErDiagram feautre is introduced, we can declare application level Entity Relationship and their default dataloader, and loaders will be applied automatically.

It could be seamlessly integrated with modern Python web frameworks including FastAPI, Litestar, and Django-ninja.

For FastAPI developers, we can visualize the dependencies of schemas by installing fastapi-voyager, visit live demo

Installation

# latest v1
pip install pydantic-resolve==1.13.5

# v2
pip install pydantic-resolve

Starting from pydantic-resolve v1.11.0, both pydantic v1 and v2 are supported.

Starting from pydantic-resolve v2.0.0, it only supports pydantic v2, pydantic v1 and dataclass are dropped, anything else are backward compatible.

Hello world

Getting teams along with sprints and memebers on-demand, using dataloader to batch load related data which eliminate N+1 query.

from pydantic_resolve import Loader, Resolver

class Sample1TeamDetail(tms.Team):
    sprints: list[Sample1SprintDetail] = []
    def resolve_sprints(self, loader=Loader(spl.team_to_sprint_loader)):
        return loader.load(self.id)
    
    members: list[us.User] = []
    def resolve_members(self, loader=Loader(ul.team_to_user_loader)):
        return loader.load(self.id)

@route.get('/teams-with-detail', response_model=List[Sample1TeamDetail])
async def get_teams_with_detail(session: AsyncSession = Depends(db.get_session)):
    teams = await tmq.get_teams(session)
    teams = [Sample1TeamDetail.model_validate(t) for t in teams]
    teams = await Resolver().resolve(teams)
    return teams

ER diagram

pydantic-resolve provided a powerful feature to define application level ER diagram, it's based on Entity and Relationships.

Inside Relationship we can describe many things like load, load_many, multiple relationship or primitive loader.

Those cases are listed inside test file: https://github.com/allmonday/pydantic-resolve/blob/master/tests/er_diagram/test_er_diagram_inline.py#L85

Once after we have it defined source code:

diagram = ErDiagram(
    configs=[
        Entity(
            kls=Team,
            relationships=[
                Relationship( field='id', target_kls=list[Sprint], loader=sprint_loader.team_to_sprint_loader),
                Relationship( field='id', target_kls=list[User], loader=user_loader.team_to_user_loader)
            ]
        ),
        Entity(
            kls=Sprint,
            relationships=[
                Relationship( field='id', target_kls=list[Story], loader=story_loader.sprint_to_story_loader)
            ]
        ),
        Entity(
            kls=Story,
            relationships=[
                Relationship( field='id', target_kls=list[Task], loader=task_loader.story_to_task_loader),
                Relationship( field='owner_id', target_kls=User, loader=user_loader.user_batch_loader)
            ]
        ),
        Entity(
            kls=Task,
            relationships=[
                Relationship( field='owner_id', target_kls=User, loader=user_loader.user_batch_loader)
            ]
        )
    ]
)

We can view it inside fastapi-voyager

Then the code above can be simplified as, The required dataloader will be automatically inferred.

# old
class Sample1TeamDetail(tms.Team):
    sprints: list[Sample1SprintDetail] = []
    def resolve_sprints(self, loader=Loader(spl.team_to_sprint_loader)):
        return loader.load(self.id)
    
    members: list[us.User] = []
    def resolve_members(self, loader=Loader(ul.team_to_user_loader)):
        return loader.load(self.id)

# new
class Sample1TeamDetail(tms.Team):
    sprints: Annotated[list[Sample1SprintDetail], LoadBy('id')] = []
    members: Annotated[list[us.User], LoadBy('id')] = []

It also support SqlAlchemy ORM Base style: https://github.com/allmonday/pydantic-resolve/blob/master/tests/er_diagram/test_er_diagram_inline.py

Use 3 steps to construct complex data

Let's take Agile's model for example, it includes Story, Task and User, here is a live demo and source code

1. Define entities and relationships

Establish entity relationships model based on business concept.

from pydantic import BaseModel

class Story(BaseModel):    
    id: int
    name: str
    owner_id: int
    sprint_id: int

    model_config = ConfigDict(from_attributes=True)

class Task(BaseModel):
    id: int
    name: str
    owner_id: int
    story_id: int
    estimate: int

    model_config = ConfigDict(from_attributes=True)

class User(BaseModel):
    id: int
    name: str
    level: str

    model_config = ConfigDict(from_attributes=True)

The dataloader is defined for general usage, if other approach such as ORM relationship is available, it can be easily replaced. DataLoader's implementation supports all kinds of data sources, from database queries to microservice RPC calls.

from .model import Task
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select
import src.db as db
from pydantic_resolve import build_list

# --------- user_id -> user ----------
async def batch_get_users_by_ids(session: AsyncSession, user_ids: list[int]):
    users = (await session.execute(select(User).where(User.id.in_(user_ids)))).scalars().all()
    return users

async def user_batch_loader(user_ids: list[int]):
    async with db.async_session() as session:
        users = await batch_get_users_by_ids(session, user_ids)
        return build_object(users, user_ids, lambda u: u.id)

# ---------- task id -> task ------------
async def batch_get_tasks_by_ids(session: AsyncSession, story_ids: list[int]):
    users = (await session.execute(select(Task).where(Task.story_id.in_(story_ids)))).scalars().all()
    return users

async def story_to_task_loader(story_ids: list[int]):
    async with db.async_session() as session:
        tasks = await batch_get_tasks_by_ids(session, story_ids)
        return build_list(tasks, story_ids, lambda u: u.story_id)

ErDiagram can help declare the entity relationships, and fastapi-voyager can display it.

diagram = ErDiagram(
    configs=[
        ErConfig(
            kls=Story,
            relationships=[
                Relationship( field='id', target_kls=list[Task], loader=task_loader.story_to_task_loader),
                Relationship( field='owner_id', target_kls=User, loader=user_loader.user_batch_loader)
            ]
        ),
        ErConfig(
            kls=Task,
            relationships=[
                Relationship( field='owner_id', target_kls=User, loader=user_loader.user_batch_loader)
            ]
        )
    ]
)

config_global_resolver(diagram)  # inject into Resolver

2. Compose core business data structure.

We can simpliy inherit or use DefineSubset to reuse Entity fields and extends new field and resolve them by dataloaders.

view in voyager

If ErDiagram is not provided, we need to manually choose the loader:

class Task(BaseTask):
    user: Optional[BaseUser] = None
    def resolve_user(self, loader=Loader(user_batch_loader)):
        return loader.load(self.owner_id) if self.owner_id else None

class Story(BaseStory):
    tasks: list[Task] = []
    def resolve_tasks(self, loader=Loader(story_to_task_loader)):
        return loader.load(self.id)

    assignee: Optional[BaseUser] = None
    def resolve_assignee(self, loader=Loader(user_batch_loader)):
        return loader.load(self.owner_id) if self.owner_id else None

If ErDiagram is provided, we just need to provide the name of foreign key

class Task(BaseTask):
    user: Annotated[Optional[BaseUser], LoadBy('owner_id')] = None

class Story(BaseStory):
    tasks: Annotated[list[Task], LoadBy('id')] = []
    assignee: Annotated[Optional[BaseUser], LoadBy('owner_id')] = None        

ensure_subset decorator is a helper function which ensures the target class's fields (without default value) are strictly subset of class in parameter.

Meta class DefineSubset can be used to define schema with picked fields.

class Story1(DefineSubset):
    # define the base class and fields wanted
    __pydantic_resolve_subset__ = (BaseStory, ('id', 'name', 'owner_id'))

    tasks: Annotated[list[Task1], LoadBy('id')] = []
    assignee: Annotated[Optional[BaseUser], LoadBy('owner_id')] = None

3. Make additional transformations based on business requirements.

Dataset from base entities can not meet all requirements, adding extra computed fields or adjusting current data are common requirements.

post_method is what we need, it is triggered after all descendant nodes are resolved.

It could read fields from ancestor, collect fields from descendants or modify the data fetched by resolve method.

Let's show them case by case.

#1: Compute new fields from current data

view in voyager, double click Story2

post methods are executed after all resolve_methods are resolved, so we can use it to calculate extra fields.

class Task2(BaseTask):
    user: Annotated[Optional[BaseUser], LoadBy('owner_id')] = None

class Story2(DefineSubset):
    __pydantic_resolve_subset__ = (BaseStory, ('id', 'name', 'owner_id'))

    tasks: Annotated[list[Task2], LoadBy('id')] = []
    assignee: Annotated[Optional[BaseUser], LoadBy('owner_id')] = None

    total_estimate: int = 0
    def post_total_estimate(self):
        return sum(task.estimate for task in self.tasks)

#2: Collect items from descendants

view in voyager, double click Task1, choose source code

__pydantic_resolve_collect__ can collect fields from current node and then send them to ancestor node who declared related_users.

class Task1(BaseTask):
    __pydantic_resolve_collect__ = {'user': 'related_users'}  # Propagate user to collector: 'related_users'

    user: Annotated[Optional[BaseUser], LoadBy('owner_id')] = None

class Story1(DefineSubset):
    __pydantic_resolve_subset__ = (BaseStory, ('id', 'name', 'owner_id'))

    tasks: Annotated[list[Task1], LoadBy('id')] = []
    assignee: Annotated[Optional[BaseUser], LoadBy('owner_id')] = None

    related_users: list[BaseUser] = []
    def post_related_users(self, collector=Collector(alias='related_users')):
        return collector.values()

#3: Propagate ancestor data to descendants through ancestor_context

view in voyager, double click Story3

__pydantic_resolve_expose__ could expose specific fields from current node to it's descendant.

alias_names should be global unique inside root node.

descendant nodes could read the value with ancestor_context[alias_name].

source code

# post case 1
class Task3(BaseTask):
    user: Annotated[Optional[BaseUser], LoadBy('owner_id')] = None

    fullname: str = ''
    def post_fullname(self, ancestor_context):  # Access story.name from parent context
        return f'{ancestor_context["story_name"]} - {self.name}'

class Story3(DefineSubset):
    __pydantic_resolve_subset__ = (BaseStory, ('id', 'name', 'owner_id'))
    __pydantic_resolve_expose__ = {'name': 'story_name'}

    tasks: Annotated[list[Task3], LoadBy('id')] = []
    assignee: Annotated[Optional[BaseUser], LoadBy('owner_id')] = None

4. Run with resolver

from pydantic_resolve import Resolver

stories = [Story(**s) for s in await query_stories()]
data = await Resolver().resolve(stories)

query_stories() returns BaseStory list, after we transformed it into Story, resolve and post fields are initialized as default value, after Resolver().resolve() finished, all these fields will be resolved and post-processed to what we expected.

How it works

The process is similar to breadth-first traversal, with additional hooks after the traversal of descendant nodes is completed.

Compared with GraphQL, both traverse descendant nodes recursively and support resolver functions and DataLoaders. The key difference is post-processing: from the post-processing perspective, resolved data is always ready for further transformation, regardless of whether it came from resolvers or initial input.

pydantic class can be initialized by deep nested data (which means descendant are provided in advance), then just need to run the post process.

Within post hooks, developers can read descendant data, adjust existing fields, compute derived fields.

Post hooks also enable bidirectional data flow: they can read from ancestor nodes and push values up to ancestors, which is useful for adapting data to varied business requirements.

Documentation

• Documentation: https://allmonday.github.io/pydantic-resolve/
• Composition-Oriented Pattern: https://github.com/allmonday/composition-oriented-development-pattern
• Live demo: https://www.newsyeah.fun/voyager/?tag=sample_1
• Resolver Pattern: A Better Alternative to GraphQL in BFF (api-integration).

Testing and Coverage

tox

tox -e coverage
python -m http.server

Current test coverage: 97%