# Pydantic Resolve


> A tool for building Domain layer modeling and use case assembly.


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[中文版](./README.zh.md)

## What is this?

**pydantic-resolve** is a Pydantic-based data construction tool that enables you to assemble complex data structures **declaratively** without writing boring imperative glue code.

### What problem does it solve?

Consider this scenario: you need to provide API data to frontend clients from multiple data sources (databases, RPC services, etc.) that requires composition, transformation, and computation. How would you typically approach this?

First, let's define the response schemas:

```python
from pydantic import BaseModel
from typing import Optional, List

class UserResponse(BaseModel):
    id: int
    name: str
    email: str

class TaskResponse(BaseModel):
    id: int
    name: str
    owner_id: int
    owner: Optional[UserResponse] = None

class SprintResponse(BaseModel):
    id: int
    name: str
    tasks: List[TaskResponse] = []

class TeamResponse(BaseModel):
    id: int
    name: str
    sprints: List[SprintResponse] = []
    total_tasks: int = 0
```

Now, let's see how to populate these schemas with data:

```python
# Traditional approach: imperative data assembly with Pydantic schemas
async def get_teams_with_detail(session):
    # 1. Fetch team list from database
    teams_data = await session.execute(select(Team))
    teams_data = teams_data.scalars().all()

    # 2. Build response objects and fetch related data imperatively
    teams = []
    for team_data in teams_data:
        team = TeamResponse(**team_data.__dict__)

        # Fetch sprints for this team
        sprints_data = await get_sprints_by_team(session, team.id)
        team.sprints = []

        for sprint_data in sprints_data:
            sprint = SprintResponse(**sprint_data.__dict__)

            # Fetch tasks for this sprint
            tasks_data = await get_tasks_by_sprint(session, sprint.id)
            sprint.tasks = []

            for task_data in tasks_data:
                task = TaskResponse(**task_data.__dict__)

                # Fetch owner for this task
                owner_data = await get_user_by_id(session, task.owner_id)
                task.owner = UserResponse(**owner_data.__dict__)

                sprint.tasks.append(task)

            team.sprints.append(sprint)

        # Calculate statistics
        team.total_tasks = sum(len(sprint.tasks) for sprint in team.sprints)
        teams.append(team)

    return teams
```

**Problems**:
- Extensive nested loops
- N+1 query problem (poor performance)
- Difficult to maintain and extend
- Data fetching logic mixed with business logic

**The pydantic-resolve approach**:

```python
# Declarative: describe what you want, not how to do it
class TaskResponse(BaseModel):
    id: int
    name: str
    owner_id: int

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

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

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

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

    sprints: list[SprintResponse] = []
    def resolve_sprints(self, loader=Loader(team_to_sprints_loader)):
        return loader.load(self.id)

    # Calculate statistics automatically after sprints are loaded
    total_tasks: int = 0
    def post_total_tasks(self):
        return sum(len(sprint.tasks) for sprint in self.sprints)

# Usage
teams = await query_teams_from_db(session)
result = await Resolver().resolve(teams)
```

**Advantages**:
- Automatic batch loading (using DataLoader pattern)
- No N+1 query problem
- Clear separation of data fetching logic
- Easy to extend and maintain

### Core Features

- **Declarative data composition**: Declare how to fetch related data via `resolve_{field}` methods
- **Automatic batch loading**: Built-in DataLoader automatically batches queries to avoid N+1 issues
- **Data post-processing**: Transform and compute data after fetching via `post_{field}` methods
- **Cross-layer data passing**: Parent nodes can expose data to descendants, children can collect data to parents
- **Entity Relationship Diagram (ERD)**: Define entity relationships and auto-generate resolution logic
- **Framework integration**: Seamless integration with FastAPI, Litestar, Django Ninja

## Quick Start

### Installation

```bash
pip install pydantic-resolve
```

> Note: pydantic-resolve v2+ only supports Pydantic v2

### Step 1: Define Data Loaders

First, you need to define batch data loaders (this is the Python implementation of Facebook's DataLoader pattern):

```python
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select
from pydantic_resolve import build_list

# Batch fetch users
async def batch_get_users(session: AsyncSession, user_ids: list[int]):
    result = await session.execute(select(User).where(User.id.in_(user_ids)))
    return result.scalars().all()

# User loader
async def user_batch_loader(user_ids: list[int]):
    async with get_db_session() as session:
        users = await batch_get_users(session, user_ids)
        # Map user list to corresponding IDs
        return build_list(users, user_ids, lambda u: u.id)

# Batch fetch team tasks
async def batch_get_tasks_by_team(session: AsyncSession, team_ids: list[int]):
    result = await session.execute(select(Task).where(Task.team_id.in_(team_ids)))
    return result.scalars().all()

# Team task loader
async def team_to_tasks_loader(team_ids: list[int]):
    async with get_db_session() as session:
        tasks = await batch_get_tasks_by_team(session, team_ids)
        return build_list(tasks, team_ids, lambda t: t.team_id)
```

### Step 2: Define Response Models

Use Pydantic BaseModel to define response structures and declare how to fetch related data via `resolve_` prefixed methods:

```python
from typing import Optional, List
from pydantic import BaseModel
from pydantic_resolve import Resolver, Loader

class UserResponse(BaseModel):
    id: int
    name: str
    email: str

class TaskResponse(BaseModel):
    id: int
    name: str
    owner_id: int

    # Declaration: fetch owner via owner_id
    owner: Optional[UserResponse] = None
    def resolve_owner(self, loader=Loader(user_batch_loader)):
        return loader.load(self.owner_id)

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

    # Declaration: fetch all tasks for this team via team_id
    tasks: List[TaskResponse] = []
    def resolve_tasks(self, loader=Loader(team_to_tasks_loader)):
        return loader.load(self.id)
```

### Step 3: Use Resolver to Resolve Data

```python
from fastapi import FastAPI, Depends

app = FastAPI()

@app.get("/teams", response_model=List[TeamResponse])
async def get_teams():
    # 1. Fetch base data from database (multiple teams)
    teams_data = await get_teams_from_db()

    # 2. Convert to Pydantic models
    teams = [TeamResponse.model_validate(t) for t in teams_data]

    # 3. Resolve all related data
    result = await Resolver().resolve(teams)

    return result
```

That's it! Resolver will automatically:
1. Discover all `resolve_` methods
2. **Collect all task IDs needed by teams** (e.g., 3 teams require 3 task fetches)
3. **Batch call the corresponding loader** (one query to load all tasks instead of 3)
4. Populate results to corresponding fields

**The power of DataLoader**:
```python
# Assume 3 teams, each with multiple tasks
# Traditional approach: 3 queries
SELECT * FROM tasks WHERE team_id = 1
SELECT * FROM tasks WHERE team_id = 2
SELECT * FROM tasks WHERE team_id = 3

# DataLoader approach: 1 query
SELECT * FROM tasks WHERE team_id IN (1, 2, 3)
```

## Core Concepts Deep Dive

### DataLoader: The Secret Weapon for Batch Loading

**Problem**: Traditional related data loading leads to N+1 queries

```python
# Wrong example: N+1 queries
for task in tasks:
    task.owner = await get_user_by_id(task.owner_id)  # Generates N queries
```

**Solution**: DataLoader batch loading

```python
# DataLoader automatically batches requests
tasks = [Task1(owner_id=1), Task2(owner_id=2), Task3(owner_id=1)]

# DataLoader will merge these requests into one query:
# SELECT * FROM users WHERE id IN (1, 2)
```

### resolve Methods: Declare Data Dependencies

`resolve_{field_name}` methods are used to declare how to fetch data for that field:

```python
class CommentResponse(BaseModel):
    id: int
    content: str
    author_id: int

    # Resolver will automatically call this method and assign the return value to author field
    author: Optional[UserResponse] = None
    def resolve_author(self, loader=Loader(user_batch_loader)):
        return loader.load(self.author_id)
```

### post Methods: Data Post-Processing

After all `resolve_` methods complete execution, `post_{field_name}` methods are called. This can be used for:

- Computing derived fields
- Formatting data
- Aggregating child node data

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

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

    # After tasks are loaded, calculate total task count
    total_tasks: int = 0
    def post_total_tasks(self):
        return len(self.tasks)

    # Calculate sum of all task estimates
    total_estimate: int = 0
    def post_total_estimate(self):
        return sum(task.estimate for task in self.tasks)
```

### Cross-Layer Data Passing

**Scenario**: Child nodes need to access parent node data, or parent nodes need to collect child node data

#### Expose: Parent Nodes Expose Data to Child Nodes

```python
from pydantic_resolve import ExposeAs

class StoryResponse(BaseModel):
    id: int
    name: Annotated[str, ExposeAs('story_name')]  # Expose to child nodes

    tasks: List[TaskResponse] = []

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

    # Both post/resolve methods can access data exposed by ancestor nodes
    full_name: str = ""
    def post_full_name(self, ancestor_context):
        # Get parent (Story) name
        story_name = ancestor_context.get('story_name')
        return f"{story_name} - {self.name}"
```

#### Collect: Child Nodes Send Data to Parent Nodes

```python
from pydantic_resolve import Collector, SendTo

class TaskResponse(BaseModel):
    id: int
    owner_id: int

    # Load owner data and send to parent's related_users collector
    owner: Annotated[Optional[UserResponse], SendTo('related_users')] = None
    def resolve_owner(self, loader=Loader(user_batch_loader)):
        return loader.load(self.owner_id)

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

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

    # Collect all child node owners
    related_users: List[UserResponse] = []
    def post_related_users(self, collector=Collector(alias='related_users')):
        return collector.values()
```

## Advanced Usage

### Using Entity Relationship Diagram (ERD)

For complex applications, you can define entity relationships at the application level and automatically generate resolution logic:

```python
from pydantic_resolve import base_entity, Relationship, LoadBy, config_global_resolver

# 1. Define base entities
BaseEntity = base_entity()

class Story(BaseModel, BaseEntity):
    __relationships__ = [
        # Define relationship: load all tasks for this story via id field
        Relationship(field='id', target_kls=list['Task'], loader=story_to_tasks_loader),
        # Define relationship: load owner via owner_id field
        Relationship(field='owner_id', target_kls='User', loader=user_batch_loader),
    ]

    id: int
    name: str
    owner_id: int
    sprint_id: int

class Task(BaseModel, BaseEntity):
    __relationships__ = [
        Relationship(field='owner_id', target_kls='User', loader=user_batch_loader),
    ]

    id: int
    name: str
    owner_id: int
    story_id: int
    estimate: int

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

# 2. Generate ER diagram and register to global Resolver
diagram = BaseEntity.get_diagram()
config_global_resolver(diagram)

# 3. When defining response models, no need to write resolve methods
class TaskResponse(BaseModel):
    id: int
    name: str
    owner_id: int

    # LoadBy automatically finds relationship definitions in ERD
    owner: Annotated[Optional[User], LoadBy('owner_id')] = None

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

    tasks: Annotated[List[TaskResponse], LoadBy('id')] = []
    owner: Annotated[Optional[User], LoadBy('owner_id')] = None

# 4. Use directly
stories = await query_stories_from_db(session)
result = await Resolver().resolve(stories)
```

Advantages:
- Centralized relationship definition management
- More concise response models
- Type-safe
- Visualizable dependencies (with fastapi-voyager)

### Defining Data Subsets

If you only want to return a subset of entity fields, you can use `DefineSubset`:

```python
from pydantic_resolve import DefineSubset

# Assume you have a complete User model
class FullUser(BaseModel):
    id: int
    name: str
    email: str
    password_hash: str
    created_at: datetime
    updated_at: datetime

# Select only required fields
class UserSummary(DefineSubset):
    __subset__ = (FullUser, ('id', 'name', 'email'))

# Auto-generates:
# class UserSummary(BaseModel):
#     id: int
#     name: str
#     email: str
```

### Advanced Subset Configuration: SubsetConfig

For more complex configurations (like exposing fields to child nodes simultaneously), use `SubsetConfig`:

```python
from pydantic_resolve import DefineSubset, SubsetConfig

class StoryResponse(DefineSubset):
    __subset__ = SubsetConfig(
        kls=StoryEntity,              # Source model
        fields=['id', 'name', 'owner_id'],  # Fields to include
        expose_as=[('name', 'story_name')],  # Alias exposed to child nodes
        send_to=[('id', 'story_id_collector')]  # Send to collector
    )

# Equivalent to:
# class StoryResponse(BaseModel):
#     id: Annotated[int, SendTo('story_id_collector')]
#     name: Annotated[str, ExposeAs('story_name')]
#     owner_id: int
#
```

## Performance Optimization Tips

### Database Session Management

When using FastAPI + SQLAlchemy, pay attention to session lifecycle:

```python
@router.get("/teams", response_model=List[TeamResponse])
async def get_teams(session: AsyncSession = Depends(get_session)):
    # 1. Fetch base data (multiple teams)
    teams = await get_teams_from_db(session)

    # 2. Release session immediately (avoid deadlock)
    await session.close()

    # 3. Loaders inside Resolver will create new sessions
    teams = [TeamResponse.model_validate(t) for t in teams]
    result = await Resolver().resolve(teams)

    return result
```

### Batch Loading Optimization

Ensure your loader correctly implements batch loading:

```python
# Correct: batch load with IN query
async def user_batch_loader(user_ids: list[int]):
    async with get_session() as session:
        result = await session.execute(
            select(User).where(User.id.in_(user_ids))
        )
        users = result.scalars().all()
        return build_list(users, user_ids, lambda u: u.id)
```

**Advanced: Optimize Query Fields with `_query_meta`**

DataLoader can access required field information via `self._query_meta` to query only necessary data:

```python
from aiodataloader import DataLoader

class UserLoader(DataLoader):
    async def batch_load_fn(self, user_ids: list[int]):
        # Get fields required by response model
        required_fields = self._query_meta.get('fields', ['*'])

        # Query only required fields (optimize SQL query)
        async with get_session() as session:
            # If fields specified, query only those fields
            if required_fields != ['*']:
                columns = [getattr(User, f) for f in required_fields]
                result = await session.execute(
                    select(*columns).where(User.id.in_(user_ids))
                )
            else:
                result = await session.execute(
                    select(User).where(User.id.in_(user_ids))
                )

            users = result.scalars().all()
            return build_list(users, user_ids, lambda u: u.id)
```

**Advantages**:
- If `UserResponse` only needs `id` and `name`, SQL queries only these two fields
- Reduce data transfer and memory usage
- Improve query performance, especially for tables with many fields

**Note**: `self._query_meta` is populated after Resolver's first scan.

## Real-World Example

### Scenario: Project Management System

Requirements: Fetch all Sprints for a team, including:
- All Stories for each Sprint
- All Tasks for each Story
- Owner for each Task
- Statistics for each layer (total tasks, total estimates, etc.)

```python
from pydantic import BaseModel, ConfigDict
from typing import Optional, List
from pydantic_resolve import (
    Resolver, Loader, LoadBy,
    ExposeAs, Collector, SendTo,
    base_entity, Relationship, config_global_resolver,
    build_list, DefineSubset, SubsetConfig
)
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select

# 0. Define data loaders
async def user_batch_loader(user_ids: list[int]):
    """Batch load users"""
    async with get_db_session() as session:
        result = await session.execute(select(User).where(User.id.in_(user_ids)))
        users = result.scalars().all()
        return build_list(users, user_ids, lambda u: u.id)

async def story_to_tasks_loader(story_ids: list[int]):
    """Batch load Tasks for Stories"""
    async with get_db_session() as session:
        result = await session.execute(select(Task).where(Task.story_id.in_(story_ids)))
        tasks = result.scalars().all()
        return build_list(tasks, story_ids, lambda t: t.story_id)

async def sprint_to_stories_loader(sprint_ids: list[int]):
    """Batch load Stories for Sprints"""
    async with get_db_session() as session:
        result = await session.execute(select(Story).where(Story.sprint_id.in_(sprint_ids)))
        stories = result.scalars().all()
        return build_list(stories, sprint_ids, lambda s: s.sprint_id)

# 1. Define entities and ERD
BaseEntity = base_entity()

class UserEntity(BaseModel):
    """User entity"""
    id: int
    name: str
    email: str

class TaskEntity(BaseModel, BaseEntity):
    """Task entity"""
    __relationships__ = [
        Relationship(field='owner_id', target_kls=UserEntity, loader=user_batch_loader)
    ]
    id: int
    name: str
    owner_id: int
    story_id: int
    estimate: int

class StoryEntity(BaseModel, BaseEntity):
    """Story entity"""
    __relationships__ = [
        Relationship(field='id', target_kls=list[TaskEntity], loader=story_to_tasks_loader),
        Relationship(field='owner_id', target_kls=UserEntity, loader=user_batch_loader)
    ]
    id: int
    name: str
    owner_id: int
    sprint_id: int

class SprintEntity(BaseModel, BaseEntity):
    """Sprint entity"""
    __relationships__ = [
        Relationship(field='id', target_kls=list[StoryEntity], loader=sprint_to_stories_loader)
    ]
    id: int
    name: str
    team_id: int

# Register ERD
config_global_resolver(BaseEntity.get_diagram())

# 2. Define response models (use DefineSubset to select fields from entities)

# Base user response
class UserResponse(DefineSubset):
    __subset__ = (UserEntity, ('id', 'name'))

# Scenario 1: Basic data composition - Use LoadBy to auto-resolve related data
class TaskResponse(DefineSubset):
    __subset__ = SubsetConfig(
        kls=TaskEntity,
        fields=['id', 'name', 'estimate', 'owner_id']
    )

    # LoadBy auto-resolves owner based on Relationship definition in ERD
    owner: Annotated[Optional[UserResponse], LoadBy('owner_id')] = None

# Scenario 2: Parent exposes data to child nodes - Task names need Story prefix
class TaskResponseWithPrefix(DefineSubset):
    __subset__ = SubsetConfig(
        kls=TaskEntity,
        fields=['id', 'name', 'estimate', 'owner_id']
    )

    owner: Annotated[Optional[UserResponse], LoadBy('owner_id')] = None

    # post method can access data exposed by ancestor nodes
    full_name: str = ""
    def post_full_name(self, ancestor_context):
        # Get story_name exposed by parent (Story)
        story_name = ancestor_context.get('story_name')
        return f"{story_name} - {self.name}"

# Scenario 3: Compute extra fields - Story needs to calculate total estimate of all Tasks
class StoryResponse(DefineSubset):
    __subset__ = SubsetConfig(
        kls=StoryEntity,
        fields=['id', 'name', 'owner_id'],
        expose_as=[('name', 'story_name')]  # Expose to child nodes (used by Scenario 2)
    )

    # LoadBy auto-resolves tasks based on Relationship definition in ERD
    tasks: Annotated[List[TaskResponse], LoadBy('id')] = []

    # post_ method executes after all resolve_ methods complete
    total_estimate: int = 0
    def post_total_estimate(self):
        return sum(t.estimate for t in self.tasks)

# Scenario 4: Parent collects data from child nodes - Story needs to collect all involved developers
class TaskResponseForCollect(DefineSubset):
    __subset__ = SubsetConfig(
        kls=TaskEntity,
        fields=['id', 'name', 'estimate', 'owner_id'],
    )

    owner: Annotated[Optional[UserResponse], LoadBy('owner_id'), SendTo('related_users')] = None

class StoryResponseWithCollect(DefineSubset):
    __subset__ = (StoryEntity, ('id', 'name', 'owner_id'))

    tasks: Annotated[List[TaskResponseForCollect], LoadBy('id')] = []

    # Collect all child node owners
    related_users: List[UserResponse] = []
    def post_related_users(self, collector=Collector(alias='related_users')):
        return collector.values()

# Sprint response model - Combines all above features
class SprintResponse(DefineSubset):
    __subset__ = (SprintEntity, ('id', 'name'))

    # Use LoadBy to auto-resolve stories
    stories: Annotated[List[StoryResponse], LoadBy('id')] = []

    # Calculate statistics (total estimate of all stories)
    total_estimate: int = 0
    def post_total_estimate(self):
        return sum(s.total_estimate for s in self.stories)

# 3. API endpoint
@app.get("/sprints", response_model=List[SprintResponse])
async def get_sprints(session: AsyncSession = Depends(get_session)):
    """Fetch all Sprints with complete hierarchical data"""
    sprints_data = await get_sprints_from_db(session)
    await session.close()

    sprints = [SprintResponse.model_validate(s) for s in sprints_data]
    result = await Resolver().resolve(sprints)

    return result
```

**Architectural Advantages**:
- **Entity-Response Separation**: Entities define business entities and relationships, Responses define API return structures
- **Reusable Relationship Definitions**: Define relationships once via ERD, all response models can use `LoadBy` for auto-resolution
- **Type Safety**: DefineSubset ensures field types are inherited from entities
- **Flexible Composition**: Define different response models based on the same entities and reuse DataLoader
- **Query Optimization**: DataLoader can access required field info via `self._query_meta` to query only necessary data (e.g., SQL `SELECT` only required columns)

**Scenario Coverage**:
- **Scenario 1**: Basic data composition - Auto-resolve related data
- **Scenario 2**: Expose - Parent nodes expose data to child nodes (e.g., Task uses Story's name)
- **Scenario 3**: post - Compute extra fields (e.g., calculate total estimates)
- **Scenario 4**: Collect - Parent nodes collect data from child nodes (e.g., collect all developers)

Each scenario is independent and reusable, can be combined as needed.

## Visualizing Dependencies with fastapi-voyager

**pydantic-resolve** works best with [fastapi-voyager](https://github.com/allmonday/fastapi-voyager) - a powerful visualization tool that makes complex data relationships easy to understand.

### Why fastapi-voyager?
<img width="1564" height="770" alt="image" src="https://github.com/user-attachments/assets/12d9e664-8ae0-4f8f-a99a-c533245e75cb" />

<img width="1463" height="521" alt="image" src="https://github.com/user-attachments/assets/739c7ae7-3fbf-4a92-afca-39ab61fe87f5" />


pydantic-resolve's declarative approach hides execution details, which can make it hard to understand **what's happening under the hood**. fastapi-voyager solves this by:

- **Color-coded operations**: See `resolve`, `post`, `expose`, and `collect` at a glance
- **Interactive exploration**: Click nodes to highlight upstream/downstream dependencies
- **ERD visualization**: View entity relationships defined in your data models
- **Source code navigation**: Double-click any node to jump to its definition
- **Quick search**: Find models and trace their relationships instantly

### Installation

```bash
pip install fastapi-voyager
```

### Basic Setup

```python
from fastapi import FastAPI
from fastapi_voyager import create_voyager

app = FastAPI()

# Mount voyager to visualize your API
app.mount('/voyager', create_voyager(
    app,
    enable_pydantic_resolve_meta=True  # Show pydantic-resolve metadata
))
```

Visit `http://localhost:8000/voyager` to see the interactive visualization!

### Understanding the Visualization

When you enable `enable_pydantic_resolve_meta=True`, fastapi-voyager uses color-coded markers to show pydantic-resolve operations:

#### Field Markers

- **● resolve** - Field data is loaded via `resolve_{field}` method or `LoadBy`
- **● post** - Field is computed via `post_{field}` method after all resolves complete
- **● expose as** - Field is exposed to descendant nodes via `ExposeAs`
- **● send to** - Field data is sent to parent collectors via `SendTo`
- **● collectors** - Field collects data from child nodes via `Collector`

#### Example

```python
class TaskResponse(BaseModel):
    id: int
    name: str
    owner_id: int

    # resolve: loaded via DataLoader
    owner: Annotated[Optional[UserResponse], LoadBy('owner_id')] = None

    # send to: owner data sent to parent's collector
    owner: Annotated[Optional[UserResponse], LoadBy('owner_id'), SendTo('related_users')] = None

class StoryResponse(BaseModel):
    id: int

    # expose as: name exposed to descendants
    name: Annotated[str, ExposeAs('story_name')]

    # resolve: tasks loaded via DataLoader
    tasks: Annotated[List[TaskResponse], LoadBy('id')] = []

    # post: computed from tasks
    total_estimate: int = 0
    def post_total_estimate(self):
        return sum(t.estimate for t in self.tasks)

    # collectors: collects from child nodes
    related_users: List[UserResponse] = []
    def post_related_users(self, collector=Collector(alias='related_users')):
        return collector.values()
```

**In fastapi-voyager**, you'll see:
- `owner` field marked with resolve and send to
- `name` field marked with expose as: story_name
- `tasks` field marked with resolve
- `total_estimate` field marked with post
- `related_users` field marked with collectors: related_users

### Visualizing Entity Relationships (ERD)

If you're using ERD to define entity relationships, fastapi-voyager can visualize them:

```python
from pydantic_resolve import base_entity, Relationship, config_global_resolver

# Define entities with relationships
BaseEntity = base_entity()

class TaskEntity(BaseModel, BaseEntity):
    __relationships__ = [
        Relationship(field='owner_id', target_kls=UserEntity, loader=user_batch_loader)
    ]
    id: int
    name: str
    owner_id: int

class StoryEntity(BaseModel, BaseEntity):
    __relationships__ = [
        Relationship(field='id', target_kls=list[TaskEntity], loader=story_to_tasks_loader)
    ]
    id: int
    name: str

# Register ERD
diagram = BaseEntity.get_diagram()
config_global_resolver(diagram)

# Visualize it in voyager
app.mount('/voyager', create_voyager(
    app,
    er_diagram=diagram,  # Show entity relationships
    enable_pydantic_resolve_meta=True
))
```

### Interactive Features

#### Click to Highlight
Click any model or route to see:
- **Upstream**: What this model depends on
- **Downstream**: What depends on this model

#### Double-Click to View Code
Double-click any node to:
- View the source code (if configured)
- Open the file in VSCode (by default)

#### Quick Search
- Press `Shift + Click` on a node to search for it
- Use the search box to find models by name
- See related models highlighted automatically

### Pro Tips

1. **Start Simple**: Begin with `enable_pydantic_resolve_meta=False` to see the basic structure
2. **Enable Metadata**: Turn on `enable_pydantic_resolve_meta=True` to see data flow
3. **Use ERD View**: Toggle ERD view to understand entity-level relationships
4. **Trace Data Flow**: Click a node and follow the colored links to understand data dependencies

### Live Demo

Check out the [live demo](https://www.newsyeah.fun/voyager/?tag=sample_1) to see fastapi-voyager in action!

### Learn More

- [fastapi-voyager Documentation](https://github.com/allmonday/fastapi-voyager)
- [Example Project](https://github.com/allmonday/composition-oriented-development-pattern)

---

**Key Insight**: fastapi-voyager turns pydantic-resolve's "hidden magic" into **visible, understandable data flows**, making it much easier to debug, optimize, and explain your code to others!

## Why Not GraphQL?

Although pydantic-resolve is inspired by GraphQL, it's better suited as a BFF (Backend For Frontend) layer solution:

| Feature | GraphQL | pydantic-resolve |
|----------|---------|------------------|
| Performance | Requires complex DataLoader configuration | Built-in batch loading |
| Type Safety | Requires additional toolchain | Native Pydantic type support |
| Learning Curve | Steep (Schema, Resolver, Loader...) | Gentle (only need Pydantic) |
| Debugging | Difficult | Simple (standard Python code) |
| Integration | Requires additional server | Seamless integration with existing frameworks |
| Flexibility | Queries too flexible, hard to optimize | Explicit API contracts |

## More Resources

- **Full Documentation**: https://allmonday.github.io/pydantic-resolve/
- **Example Project**: https://github.com/allmonday/composition-oriented-development-pattern
- **Live Demo**: https://www.newsyeah.fun/voyager/?tag=sample_1
- **API Reference**: https://allmonday.github.io/pydantic-resolve/api/

## Development

```bash
# Clone repository
git clone https://github.com/allmonday/pydantic_resolve.git
cd pydantic_resolve

# Install development dependencies
uv venv
source .venv/bin/activate
uv pip install -e ".[dev]"

# Run tests
uv run pytest tests/

# View test coverage
tox -e coverage
```

## License

MIT License

## Author

tangkikodo (allmonday@126.com)

