pydantic-resolve 4.0.0a1

pydantic-resolve turns pydantic from a static data container into a powerful composable component.

Pydantic Resolve

Declarative data assembly for Pydantic — eliminate N+1 queries with minimal code.

中文版

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pydantic-resolve is inspired by GraphQL. It builds database-independent application-layer Entity Relationship Diagrams using DataLoader, providing rich data assembly and post-processing capabilities. It can also auto-generate GraphQL queries and MCP services.

Why pydantic-resolve?

Core capabilities:

Feature	What it does
Automatic Batching	DataLoader eliminates N+1 queries automatically
Declarative Assembly	Declare dependencies, framework handles the rest
ER Diagram + AutoLoad	Define entity relationships, auto-resolve related data
GraphQL Support	Generate schema from ERD, query with dynamic models
MCP Integration	Expose GraphQL APIs to AI agents with progressive disclosure

One line to fetch nested data:

class Task(BaseModel):
    owner_id: int
    owner: Optional[User] = None

    def resolve_owner(self, loader=Loader(user_loader)):
        return loader.load(self.owner_id)  # That's it!

# Resolver automatically batches all owner lookups into one query
result = await Resolver().resolve(tasks)

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Quick Start

Install

pip install pydantic-resolve

The N+1 Problem

# Traditional: 1 + N queries
for task in tasks:
    task.owner = await get_user(task.owner_id)  # N queries!

The pydantic-resolve Solution

from pydantic import BaseModel
from typing import Optional, List
from pydantic_resolve import Resolver, Loader, build_list

# 1. Define your loaders (batch queries)
async def user_loader(ids: list[int]):
    users = await db.query(User).filter(User.id.in_(ids)).all()
    return build_list(users, ids, lambda u: u.id)

async def task_loader(sprint_ids: list[int]):
    tasks = await db.query(Task).filter(Task.sprint_id.in_(sprint_ids)).all()
    return build_list(tasks, sprint_ids, lambda t: t.sprint_id)

# 2. Define your schema with resolve methods
class TaskResponse(BaseModel):
    id: int
    name: str
    owner_id: int

    owner: Optional[dict] = None
    def resolve_owner(self, loader=Loader(user_loader)):
        return loader.load(self.owner_id)

class SprintResponse(BaseModel):
    id: int
    name: str

    tasks: List[TaskResponse] = []
    def resolve_tasks(self, loader=Loader(task_loader)):
        return loader.load(self.id)

# 3. Resolve - framework handles batching automatically
@app.get("/sprints")
async def get_sprints():
    sprints = await get_sprint_data()
    return await Resolver().resolve([SprintResponse.model_validate(s) for s in sprints])

Result: 1 query per loader, regardless of data depth.

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Core Concepts

Resolve: Declarative Data Loading

Instead of imperative data fetching, declare what you need:

class Task(BaseModel):
    owner_id: int
    owner: Optional[User] = None

    def resolve_owner(self, loader=Loader(user_loader)):
        return loader.load(self.owner_id)

The framework:

1. Collects all owner_id values
2. Batches them into one query
3. Maps results back to correct objects

DataLoader: Automatic Batching

DataLoader batches multiple requests within the same event loop tick:

# Without DataLoader: 100 tasks = 100 user queries
# With DataLoader: 100 tasks = 1 user query (WHERE id IN (...))

async def user_loader(user_ids: list[int]):
    return await db.query(User).filter(User.id.in_(user_ids)).all()

Expose & Collect: Cross-layer Data Flow

In nested data structures, parent and child nodes often need to share data. Traditional approaches require explicit parameter passing or tight coupling. pydantic-resolve provides two declarative mechanisms:

• ExposeAs: Parent nodes expose data to all descendants (downward flow)
• SendTo + Collector: Child nodes send data to parent collectors (upward flow)

This creates a clean separation — parent doesn't need to know child's structure, and child doesn't need explicit parent references.

from pydantic_resolve import ExposeAs, Collector, SendTo
from typing import Annotated

# 1. Parent EXPOSES data to descendants (downward flow)
class Story(BaseModel):
    name: Annotated[str, ExposeAs('story_name')]
    tasks: List[Task] = []

# 2. Child ACCESSES ancestor context (no explicit parent reference needed)
class Task(BaseModel):
    def post_full_path(self, ancestor_context):
        return f"{ancestor_context['story_name']} / {self.name}"

# 3. Child SENDS data to parent collector (upward flow)
class Task(BaseModel):
    owner: Annotated[User, SendTo('contributors')] = None

class Story(BaseModel):
    contributors: List[User] = []
    def post_contributors(self, collector=Collector('contributors')):
        return collector.values()  # Auto-deduplicated list of all task owners

Use cases:

• Pass configuration/context down to nested objects (e.g., user permissions, locale)
• Aggregate results up from nested objects (e.g., collect all unique tags from posts)

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Declarative Mode: ER Diagram + AutoLoad

Quick Start and Core Concepts demonstrate pydantic-resolve's Core API: writing resolve_* methods and manually specifying Loaders. For simple use cases, this is sufficient.

When a project involves multiple interrelated entities, pydantic-resolve offers a Declarative API: define entity relationships and default loaders in an ER Diagram, then use AutoLoad to auto-generate the corresponding resolve methods.

Declarative API is built on top of Core API. AutoLoad fields generate equivalent resolve_* methods at runtime, so both modes can be freely mixed — you can still use post_* methods in Declarative Mode, or fall back to hand-written resolve_* for specific fields.

	Core API	Declarative API
Approach	Hand-write resolve_* + specify Loader	Define ER Diagram + AutoLoad
Control	Full control	Convention over configuration
Best for	Simple projects, one-off data loading	Multiple related entities, GraphQL/MCP needed
Relationships	Scattered across Response classes	Centralized in ER Diagram

Define Entities and Relationships

Create a base class with base_entity(), then define relationships in __relationships__:

from pydantic import BaseModel
from typing import Annotated, Optional
from pydantic_resolve import base_entity, Relationship, config_global_resolver

BaseEntity = base_entity()

class UserEntity(BaseModel, BaseEntity):
    id: int
    name: str

class TaskEntity(BaseModel, BaseEntity):
    __relationships__ = [
        # Loader can query Postgres, call RPC, or fetch from Redis
        # API consumers don't need to know where data comes from
        Relationship(fk='owner_id', target=UserEntity, name='owner', loader=user_loader)
    ]
    id: int
    name: str
    owner_id: int  # Internal FK, can be hidden from API

diagram = BaseEntity.get_diagram()
AutoLoad = diagram.create_auto_load()
config_global_resolver(diagram)

You can also use external declaration (ErDiagram + Entity) to separate relationship definitions from entity classes.

Use AutoLoad

After defining the ER Diagram, annotate fields with AutoLoad() in Response models:

from pydantic_resolve import DefineSubset

class TaskResponse(TaskEntity):
    owner: Annotated[Optional[UserEntity], AutoLoad()] = None
    # AutoLoad generates resolve_owner based on TaskEntity's __relationships__

# Usage is identical to Core API
result = await Resolver().resolve(tasks)

Use DefineSubset to selectively expose fields and hide internal FKs:

class TaskResponse(DefineSubset):
    __subset__ = (TaskEntity, ('id', 'name'))  # owner_id excluded
    owner: Annotated[Optional[UserEntity], AutoLoad()] = None

When to Use Declarative Mode

Declarative Mode is a good fit when:

• The project has 3+ interrelated entities
• You need to generate GraphQL schema or MCP services
• The team needs centralized relationship management
• You want to hide FK fields from API contracts

Core API is sufficient when:

• Only a few data loading requirements
• Simple data source
• No GraphQL or MCP needed

→ Full ERD-Driven Guide

Integrations

GraphQL

Generate GraphQL schema from ERD and execute queries:

from pydantic_resolve.graphql import GraphQLHandler

handler = GraphQLHandler(diagram)
result = await handler.execute("{ users { id name posts { title } } }")

→ GraphQL Documentation

MCP

Expose GraphQL APIs to AI agents:

from pydantic_resolve import AppConfig, create_mcp_server

mcp = create_mcp_server(apps=[AppConfig(name="blog", er_diagram=diagram)])
mcp.run()

→ MCP Documentation

Visualization

Interactive ERD exploration with fastapi-voyager:

from fastapi_voyager import create_voyager

app.mount('/voyager', create_voyager(app, er_diagram=diagram))

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pydantic-resolve vs GraphQL

Feature	GraphQL	pydantic-resolve
N+1 Prevention	Manual DataLoader setup	Built-in automatic batching
Type Safety	Separate schema files	Native Pydantic types
Learning Curve	Steep (Schema, Resolvers, Loaders)	Gentle (just Pydantic)
Debugging	Complex introspection	Standard Python debugging
Integration	Requires dedicated server	Works with any framework
Query Flexibility	Any client can query anything	Explicit API contracts

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Resources

• 📖 Full Documentation
• 🚀 Example Project
• 🎮 Live Demo
• 🎮 Live Demo - GraphQL
• 📚 API Reference

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License

MIT License

Author

tangkikodo ([e-mail])